human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "compressive sensing for feedback reduction in mimo broadcast channels"):
  Abstract: We propose a generic feedback channel model, and compressive sensing based opportunistic feedback protocol for feedback resource (channels) reduction in MIMO Broadcast Channels under the assumption that both feedback and downlink channels are noisy and undergo block Rayleigh fading. The feedback resources are shared and are opportunistically accessed by users who are strong (users above a certain fixed threshold). Strong users send same feedback information on all shared channels. They are identified by the base station via compressive sensing. The proposed protocol is shown to achieve the same sum-rate throughput as that achieved by dedicated feedback schemes, but with feedback channels growing only logarithmically with number of users.

Includes members:
  Member "necessary and sufficient conditions for sparsity pattern recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "harmless interpolation of noisy data in regression")
  Member "on off random access channels a compressed sensing framework":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "random access in c ran for user activity detection with limited capacity fronthaul")
  Member "how much feedback is required in mimo broadcast channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressive sensing based opportunistic protocol for throughput improvement in wireless networks")
  Member "multi user diversity vs accurate channel feedback for mimo broadcast channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fundamental limits in correlated fading mimo broadcast channels benefits of transmit correlation diversity")
  Member "multiuser mimo achievable rates with downlink training and channel state feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "beyond dirty paper coding for multi antenna broadcast channel with partial csit a rate splitting approach")

Question: Please tell me which class the source paper 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)