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 "bit allocation laws for multi antenna channel feedback quantization multi user case"):
  Abstract: This paper addresses the optimal design of limited-feedback downlink multi-user spatial multiplexing systems. A multiple-antenna base-station is assumed to serve multiple single-antenna users, who quantize and feed back their channel state information (CSI) through a shared rate-limited feedback channel. The optimization problem is cast in the form of minimizing the average transmission power at the base-station subject to users' target signal-to-interference-plus-noise ratios (SINR) and outage probability constraints. The goal is to derive the feedback bit allocations among the users and the corresponding channel magnitude and direction quantization codebooks in a high-resolution quantization regime. Toward this end, this paper develops an optimization framework using approximate analytical closed-form solutions, the accuracy of which is then verified by numerical results. The results show that, for channels in the real space, the number of channel direction quantization bits should be $(M-1)$ times the number of channel magnitude quantization bits, where $M$ is the number of base-station antennas. Moreover, users with higher requested quality-of-service (QoS), i.e. lower target outage probabilities, and higher requested downlink rates, i.e. higher target SINR's, should use larger shares of the feedback rate. It is also shown that, for the target QoS parameters to be feasible, the total feedback bandwidth should scale logarithmically with the geometric mean of the target SINR values and the geometric mean of the inverse target outage probabilities. In particular, the minimum required feedback rate is shown to increase if the users' target parameters deviate from the corresponding geometric means. Finally, the paper shows that, as the total number of feedback bits $B$ increases, the performance of the limited-feedback system approaches the perfect-CSI system as ${2^{-{B}/{M^2}}}$.

Includes members:
  Member "bit allocation law for multiantenna channel feedback quantization single user case":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "green communication via power optimized harq protocols")
  Member "adaptive limited feedback for sum rate maximizing beamforming in cooperative multicell systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rate splitting for multi antenna downlink precoder design and practical implementation")
  Member "space division multiple access with a sum feedback rate constraint":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "modeling analysis and optimization of caching in multi antenna small cell networks")
  Member "multiuser mimo achievable rates with downlink training and channel state feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond dirty paper coding for multi antenna broadcast channel with partial csit a rate splitting approach")
  Member "performance of orthogonal beamforming for sdma with limited feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an overview on resource allocation techniques for multi user mimo systems")
  Member "high snr analysis of mimo broadcast channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hybrid beamforming for large antenna arrays with phase shifter selection")

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)