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: "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
  Abstract: The sensitivity of millimeter wave (mmWave) signals to blockages is a fundamental challenge for mobile mmWave communication systems. The sudden blockage of the line-of-sight (LOS) link between the base station and the mobile user normally leads to disconnecting the communication session, which highly impacts the system reliability. Further, reconnecting the user to another LOS base station incurs high beam training overhead and critical latency problems. In this paper, we leverage machine learning tools and propose a novel solution for these reliability and latency challenges in mmWave MIMO systems. In the developed solution, the base stations learn how to predict that a certain link will experience blockage in the next few time frames using their past observations of adopted beamforming vectors. This allows the serving base station to proactively hand-over the user to another base station with a highly probable LOS link. Simulation results show that the developed deep learning based strategy successfully predicts blockage/hand-off in close to 95% of the times. This reduces the probability of communication session disconnection, which ensures high reliability and low latency in mobile mmWave systems.

Connected through hyperedge HE-1 (induced by source paper "adaptive millimeter wave communications exploiting mobility and blockage dynamics"):
  with members:
    - "mobility and blockage aware communications in millimeter wave vehicular networks"
    - "second best beam alignment via bayesian multi armed bandits"
    - "energy efficient interactive beam alignment for millimeter wave networks"
    - "online learning for position aided millimeter wave beam training"
    - "beam training and data transmission optimization in millimeter wave vehicular networks"
    - "optimal beam sweeping and communication in mobile millimeter wave networks"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"

Connected through hyperedge HE-2 (induced by source paper "learning beam codebooks with neural networks towards environment aware mmwave mimo"):
  with members:
    - "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"
    - "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"
    - "beam codebook design for 5g mmwave terminals"
    - "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"
    - "deep complex networks"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-3 (induced by source paper "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"):
  with members:
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "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"
    - "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-4 (induced by source paper "deep learning and gaussian process based band assignment in dual band systems"):
  with members:
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "classification algorithms for semi blind uplink downlink decoupling in sub 6 ghz mmwave 5g networks"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "an introduction to deep learning for the physical layer"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "remote channel inference for beamforming in ultra dense hyper cellular network"
    - "deep learning based mimo communications"

Connected through hyperedge HE-5 (induced by source paper "enabling large intelligent surfaces with compressive sensing and deep learning"):
  with members:
    - "non stationarities in extra large scale massive mimo"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "generative adversarial estimation of channel covariance in vehicular millimeter wave systems"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"
    - "performance analysis of large intelligence surfaces liss asymptotic data rate and channel hardening effects"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-6 (induced by source paper "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"):
  with members:
    - "channel extrapolation in fdd massive mimo theoretical analysis and numerical validation"
    - "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"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "massive mimo ten myths and one critical question"
    - "fingerprinting based positioning in distributed massive mimo systems"

Connected through hyperedge HE-7 (induced by source paper "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"):
  with members:
    - "mmwave beam prediction with situational awareness a machine learning approach"
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "generative adversarial estimation of channel covariance in vehicular millimeter wave systems"
    - "deep learning for massive mimo csi feedback"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "intra cluster characteristics of 28 ghz wireless channel in urban micro street canyon"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-8 (induced by source paper "viwi a deep learning dataset framework for vision aided wireless communications"):
  with members:
    - "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "virtual worlds as proxy for multi object tracking analysis"

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)