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: "bit allocation law for multiantenna channel feedback quantization single user case"
  Abstract: This paper studies the design and optimization of a limited feedback single-user system with multiple-antenna transmitter and single-antenna receiver. The design problem is cast in form of the minimizing the average transmission power at the base station subject to the user's outage probability constraint. The optimization is over the user's channel quantization codebook and the transmission power control function at the base station. Our approach is based on fixing the outage scenarios in advance and transforming the design problem into a robust system design problem. We start by showing that uniformly quantizing the channel magnitude in dB scale is asymptotically optimal, regardless of the magnitude distribution function. We derive the optimal uniform (in dB) channel magnitude codebook and combine it with a spatially uniform channel direction codebook to arrive at a product channel quantization codebook. We then optimize such a product structure in the asymptotic regime of B → ∞, where B is the total number of quantization feedback bits. The paper shows that for channels in the real space, the asymptotically optimal number of direction quantization bits should be (M-1)/2 times the number of magnitude quantization bits, where M is the number of base station antennas. We also show that the performance of the designed system approaches the performance of the perfect channel state information system as 2-2B/(M+1). For complex channels, the number of magnitude and direction quantization bits are related by a factor of (M-1) and the system performance scales as 2-B/M as B → ∞.

Connected through hyperedge HE-1 (induced by source paper "green communication via power optimized harq protocols"):
  with members:
    - "fundamental limits of cooperation"
    - "on the benefits of partial channel state information for repetition protocols in block fading channels"
    - "hybrid arq in multiple antenna slow fading channels performance limits and optimal linear dispersion code design"
    - "performance of hybrid arq in block fading channels a fixed outage probability analysis"
    - "capacity definitions of general channels with receiver side information"

Connected through hyperedge HE-2 (induced by source paper "optimal shape gain quantization for multiuser mimo systems with linear precoding"):
  with members:
    - "bit allocation laws for multi antenna channel feedback quantization multi user case"
    - "minimizing sum mse implies identical downlink and dual uplink power allocations"
    - "mimo broadcast channels with finite rate feedback"

Connected through hyperedge HE-3 (induced by source paper "bit allocation laws for multi antenna channel feedback quantization multi user case"):
  with members:
    - "adaptive limited feedback for sum rate maximizing beamforming in cooperative multicell systems"
    - "space division multiple access with a sum feedback rate constraint"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"
    - "performance of orthogonal beamforming for sdma with limited feedback"
    - "high snr analysis of mimo broadcast 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)