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: "compressed channel feedback for correlated massive mimo systems"
  Abstract: Massive multiple-input multiple-output (MIMO) is a promising approach for cellular communication due to its energy efficiency and high achievable data rate. These advantages, however, can be realized only when channel state information (CSI) is available at the transmitter. Since there are many antennas, CSI is too large to feed back without compression. To compress CSI, prior work has applied compressive sensing (CS) techniques and the fact that CSI can be sparsified. The adopted sparsifying bases fail, however, to reflect the spatial correlation and channel conditions or to be feasible in practice. In this paper, we propose a new sparsifying basis that reflects the long-term characteristics of the channel, and needs no change as long as the spatial correlation model does not change. We propose a new reconstruction algorithm for CS, and also suggest dimensionality reduction as a compression method. To feed back compressed CSI in practice, we propose a new codebook for the compressed channel quantization assuming no other-cell interference. Numerical results confirm that the proposed channel feedback mechanisms show better performance in point-to-point (single-user) and point-to-multi-point (multi-user) scenarios.

Connected through hyperedge HE-1 (induced by source paper "high dimensional stochastic gradient quantization for communication efficient edge learning"):
  with members:
    - "communication efficient distributed blockwise momentum sgd with error feedback"
    - "federated learning strategies for improving communication efficiency"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "signsgd compressed optimisation for non convex problems"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "communication efficient learning of deep networks from decentralized data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "multi resolution csi feedback with deep learning in massive mimo system"):
  with members:
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "deep learning for massive mimo csi feedback"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-3 (induced by source paper "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"):
  with members:
    - "multi band covariance interpolation with applications in massive mimo"
    - "fdd massive mimo efficient downlink probing and uplink feedback via active channel sparsification"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "millimeter wave beam selection using out of band spatial information"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "low rank covariance assisted downlink training and channel estimation for fdd massive mimo systems"
    - "the world s first real time testbed for massive mimo design implementation and validation"

Connected through hyperedge HE-4 (induced by source paper "sparse representation for wireless communications"):
  with members:
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "common codebook millimeter wave beam design designing beams for both sounding and communication with uniform planar arrays"
    - "joint channel training and feedback for fdd massive mimo systems"
    - "compressive sensing based multiuser detector for the large scale sm mimo uplink"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "compressive sensing based differential channel feedback for massive mimo"
    - "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-5 (induced by source paper "interleaving channel estimation and limited feedback for point to point systems with a large number of transmit antennas"):
  with members:
    - "interleaved training and training based transmission design for hybrid massive antenna downlink"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "antenna grouping based feedback compression for fdd based massive mimo systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "training sequence design for feedback assisted hybrid beamforming in massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "noncoherent trellis coded quantization a practical limited feedback technique for massive mimo systems"

Connected through hyperedge HE-6 (induced by source paper "channel feedback based on aod adaptive subspace codebook in fdd massive mimo systems"):
  with members:
    - "limited feedback channel estimation in massive mimo with non uniform directional dictionaries"
    - "joint channel training and feedback for fdd massive mimo systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "antenna grouping based feedback compression for fdd based massive mimo systems"
    - "the impact of beamwidth on temporal channel variation in vehicular channels and its implications"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"

Connected through hyperedge HE-7 (induced by source paper "on the feasibility of full duplex large scale mimo cellular systems"):
  with members:
    - "full duplex cloud radio access network stochastic design and analysis"
    - "multi cell massive mimo beamforming in assuring qos for large numbers of users"
    - "compact full duplex mimo radios in d2d underlaid cellular networks from system design to prototype results"
    - "massive mimo ten myths and one critical question"
    - "performance analysis of massive mimo for cell boundary users"
    - "prototyping real time full duplex radios"
    - "performance analysis of self interference cancellation methods in full duplex large scale mimo systems"
    - "opportunistic scheduling for full duplex uplink downlink networks"

Connected through hyperedge HE-8 (induced by source paper "efficient downlink channel probing and uplink feedback in fdd massive mimo systems"):
  with members:
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "channel vector subspace estimation from low dimensional projections"
    - "fundamental limits in correlated fading mimo broadcast channels benefits of transmit correlation diversity"
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "joint spatial division and multiplexing for mm wave channels"

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)