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: "multi user diversity vs accurate channel feedback for mimo broadcast channels"
  Abstract: A multiple transmit antenna, single receive antenna (per receiver) downlink channel with limited channel feedback is considered. Given a constraint on the total system-wide channel feedback, the following question is considered: is it preferable to get low-rate feedback from a large number of receivers or to receive high-rate/high-quality feedback from a smaller number of (randomly selected) receivers? Acquiring feedback from many users allows multi-user diversity to be exploited, while high-rate feedback allows for very precise selection of beamforming directions. It is shown that systems in which a limited number of users feedback high-rate channel information significantly outperform low-rate/many user systems. While capacity increases only double logarithmically with the number of users, the marginal benefit of channel feedback is very significant up to the point where the CSI is essentially perfect.

Connected through hyperedge HE-1 (induced by source paper "fundamental limits in correlated fading mimo broadcast channels benefits of transmit correlation diversity"):
  with members:
    - "a codebook based limited feedback system for large scale mimo"
    - "joint spatial division and multiplexing for mm wave channels"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "network mimo with linear zero forcing beamforming large system analysis impact of channel estimation and reduced complexity scheduling"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"

Connected through hyperedge HE-2 (induced by source paper "a codebook based limited feedback system for large scale mimo"):
  with members:
    - "noncoherent trellis coded quantization a practical limited feedback technique for massive mimo systems"

Connected through hyperedge HE-3 (induced by source paper "receive combining vs multi stream multiplexing in downlink systems with multi antenna users"):
  with members:
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "limited feedback based block diagonalization for the mimo broadcast channel"
    - "antenna combining for the mimo downlink channel"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"
    - "mimo broadcast channels with finite rate feedback"
    - "quantization bounds on grassmann manifolds and applications to mimo communications"
    - "on the user selection for mimo broadcast channels"

Connected through hyperedge HE-4 (induced by source paper "joint spatial division and multiplexing opportunistic beamforming and user grouping"):
  with members:
    - "network mimo with linear zero forcing beamforming large system analysis impact of channel estimation and reduced complexity scheduling"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"
    - "joint spatial division and multiplexing"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"

Connected through hyperedge HE-5 (induced by source paper "base station cooperation with feedback optimization a large system analysis"):
  with members:
    - "network mimo with linear zero forcing beamforming large system analysis impact of channel estimation and reduced complexity scheduling"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "spatial intercell interference cancellation with csi training and feedback"
    - "base station cooperation on the downlink large system analysis"
    - "optimization of training and feedback overhead for beamforming over block fading channels"
    - "adaptive limited feedback for sum rate maximizing beamforming in cooperative multicell systems"
    - "training and feedback optimization for multiuser mimo downlink"
    - "capacity of a multiple antenna fading channel with a quantized precoding matrix"

Connected through hyperedge HE-6 (induced by source paper "compressive sensing for feedback reduction in mimo broadcast channels"):
  with members:
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "on off random access channels a compressed sensing framework"
    - "how much feedback is required in mimo broadcast channels"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"

Connected through hyperedge HE-7 (induced by source paper "training and feedback optimization for multiuser mimo downlink"):
  with members:
    - "multiuser mimo achievable rates with downlink training and channel state feedback"
    - "mimo broadcast channels with finite rate feedback"

Connected through hyperedge HE-8 (induced by source paper "multiuser mimo achievable rates with downlink training and channel state feedback"):
  with members:
    - "channel state feedback over the mimo mac"
    - "mode switching for the multi antenna broadcast channel based on delay and channel quantization"
    - "optimized training and feedback for mimo downlink channels"
    - "channel state feedback schemes for multiuser mimo ofdm downlink"
    - "antenna combining for the mimo downlink channel"
    - "space division multiple access with a sum feedback rate constraint"
    - "mimo broadcast channels with finite rate feedback"
    - "on the capacity of fading mimo broadcast channels with imperfect transmitter side information"

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)