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 "limited rate channel state feedback for multicarrier block fading channels"):
  Abstract: The capacity of a fading channel can be substantially increased by feeding back channel state information from the receiver to the transmitter. If the feedback rate is limited, what state information to feed back and how to encode it are important questions. This paper studies power loading in a multicarrier system using no more than one bit of feedback per subchannel. The subchannels can be correlated and full channel state information is assumed at the receiver. First, a simple model with N parallel two-state (good/bad) memoryless subchannels is considered, where the channel state feedback is used to select a fixed number of subchannels to activate. The optimal feedback scheme is the solution to a vector quantization problem, and the associated performance for large N is characterized using a rate distortion function. As N increases, the loss in forward rate from the asymptotic (rate-distortion) value is shown to decrease as (logN)/N and √{(logN)/N} with optimal variable- and fixed-length feedback codes, respectively. These results are subsequently extended to parallel Rayleigh block fading subchannels, where the feedback designates a set of subchannels to be activated with equal power. Rate-distortion feedback codes are proposed for designating subsets of (good) subchannels with signal-to-noise ratios (SNRs) that exceed a threshold. The associated performance is compared with that of a simpler lossless source coding scheme, which designates groups of good subchannels, where both the group size and threshold are optimized. The rate-distortion codes can provide a significant increase in forward rate at low SNRs.

Includes members:
  Member "channel state feedback schemes for multiuser mimo ofdm downlink":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep convolutional compression for massive mimo csi feedback")
  Member "low density codes achieve the rate distortion bound":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ratq a universal fixed length quantizer for stochastic optimization")
  Member "limited feedback beamforming over temporally correlated channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "high dimensional stochastic gradient quantization for communication efficient edge learning")

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)