human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
  Abstract: Can we map the channels at one set of antennas and one frequency band to the channels at another set of antennas---possibly at a different location and a different frequency band? If this channel-to-channel mapping is possible, we can expect dramatic gains for massive MIMO systems. For example, in FDD massive MIMO, the uplink channels can be mapped to the downlink channels or the downlink channels at one subset of antennas can be mapped to the downlink channels at all the other antennas. This can significantly reduce (or even eliminate) the downlink training/feedback overhead. In the context of cell-free/distributed massive MIMO systems, this channel mapping can be leveraged to reduce the fronthaul signaling overhead as only the channels at a subset of the distributed terminals need to be fed to the central unit which can map them to the channels at all the other terminals. This mapping can also find interesting applications in mmWave beam prediction, MIMO radar, and massive MIMO based positioning. #R##N#In this paper, we introduce the new concept of channel mapping in space and frequency, where the channels at one set of antennas and one frequency band are mapped to the channels at another set of antennas and frequency band. First, we prove that this channel-to-channel mapping function exists under the condition that the mapping from the candidate user positions to the channels at the first set of antennas is bijective; a condition that can be achieved with high probability in several practical MIMO communication scenarios. Then, we note that the channel-to-channel mapping function, even if it exists, is typically unknown and very hard to characterize analytically as it heavily depends on the various elements of the surrounding environment. With this motivation, we propose to leverage the powerful learning capabilities of deep neural networks ....

Connected through hyperedge HE-1 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback"):
  with members:
    - "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "cnn based analog csi feedback in fdd mimo ofdm systems"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "multi resolution csi feedback with deep learning in massive mimo system"
    - "deep convolutional compression for massive mimo csi feedback"
    - "machine learning in the air"
    - "mimo channel information feedback using deep recurrent network"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for 5g networks joint beamforming power control and interference coordination"):
  with members:
    - "a deep learning framework for optimization of miso downlink beamforming"
    - "a framework for automated cellular network tuning with reinforcement learning"
    - "deep learning in downlink coordinated multipoint in new radio heterogeneous networks"
    - "q learning algorithm for volte closed loop power control in indoor small cells"
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning for launching and mitigating wireless jamming attacks"

Connected through hyperedge HE-3 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction"):
  with members:
    - "enabling fdd massive mimo through deep learning based channel prediction"
    - "non stationarities in extra large scale massive mimo"
    - "multi band covariance interpolation with applications in massive mimo"
    - "yolov3 an incremental improvement"
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "deep learning for massive mimo csi feedback"

Connected through hyperedge HE-4 (induced by source paper "deep transfer learning based downlink channel prediction for fdd massive mimo systems"):
  with members:
    - "fdd massive mimo based on efficient downlink channel reconstruction"
    - "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"
    - "decaying momentum helps neural network training"
    - "learning to transfer unsupervised meta domain translation"
    - "model aided deep neural network for source number detection"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"
    - "viwi a deep learning dataset framework for vision aided wireless communications"

Connected through hyperedge HE-5 (induced by source paper "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"):
  with members:
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "deep learning and gaussian process based band assignment in dual band systems"
    - "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "modeling and analyzing millimeter wave cellular systems"
    - "improved handover through dual connectivity in 5g mmwave mobile networks"

Connected through hyperedge HE-6 (induced by source paper "a survey on deep learning based techniques for modeling and estimation of massivemimo channels"):
  with members:
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "physical adversarial attacks against end to end autoencoder communication systems"
    - "deep learning for ul dl channel calibration in generic massive mimo systems"
    - "sum spectral efficiency maximization in massive mimo systems benefits from deep learning"
    - "deep learning for csi feedback based on superimposed coding"
    - "compressed csi feedback with learned measurement matrix for mmwave massive mimo"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "exploiting wireless channel state information structures beyond linear correlations a deep learning approach"

Connected through hyperedge HE-7 (induced by source paper "deep learning predictive band switching in wireless networks"):
  with members:
    - "beam codebook design for 5g mmwave terminals"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "caching meets millimeter wave communications for enhanced mobility management in 5g networks"
    - "improved handover through dual connectivity in 5g mmwave mobile networks"
    - "millimeter wave beam selection using out of band spatial information"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-8 (induced by source paper "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"):
  with members:
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "generative adversarial estimation of channel covariance in vehicular millimeter wave systems"
    - "deep complex networks"
    - "an overview of multi task learning in deep neural networks"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IT(Information Theory)