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: "quantized multimode precoding in spatially correlated multiantenna channels"
  Abstract: Multimode precoding, where the number of independent data streams is adapted optimally, can be used to maximize the achievable throughput in multiantenna communication systems. Motivated by standardization efforts embraced by the industry, the focus of this work is on systematic precoder design with realistic assumptions on the spatial correlation, channel state information (CSI) at the transmitter and the receiver, and implementation complexity. For the spatial correlation of the channel matrix, we assume a general channel model, based on physical principles, that has been verified by many recent measurement campaigns. We also assume a coherent, linear minimum mean-square error (MMSE) receiver and knowledge of the spatial statistics at the transmitter along with the presence of an ideal, low-rate feedback link from the receiver to the transmitter. The reverse link is used for codebook-index feedback and the goal of this work is to construct precoder codebooks, adaptable in response to the statistical information, such that the achievable throughput is significantly enhanced over that of a fixed, nonadaptive, independent and identically distributed (i.i.d.) codebook design. We illustrate how a codebook of semiunitary precoder matrices localized around some fixed center on the Grassmann manifold can be skewed in response to the spatial correlation via low-complexity maps that can rotate and scale submanifolds on the Grassmann manifold. The skewed codebook in combination with a low-complexity statistical power allocation scheme is then shown to bridge the gap in performance between a perfect CSI benchmark and an i.i.d. codebook design.

Connected through hyperedge HE-1 (induced by source paper "ensemble properties of rvq based limited feedback beamforming codebooks"):
  with members:
    - "statistical beamforming on the grassmann manifold for the two user broadcast channel"
    - "why does the kronecker model result in misleading capacity estimates"
    - "capacity of a multiple antenna fading channel with a quantized precoding matrix"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"
    - "mimo broadcast channels with finite rate feedback"

Connected through hyperedge HE-2 (induced by source paper "why does the kronecker model result in misleading capacity estimates"):
  with members:
    - "to code or not to code across time space time coding with feedback"
    - "low complexity structured precoding for spatially correlated mimo channels"
    - "optimal constellations for the low snr noncoherent mimo block rayleigh fading channel"
    - "on the outage capacity of correlated multiple path mimo channels"

Connected through hyperedge HE-3 (induced by source paper "low complexity structured precoding for spatially correlated mimo channels"):
  with members:
    - "to code or not to code across time space time coding with feedback"
    - "mimo broadcast channels with finite rate feedback"
    - "mutual information and minimum mean square error in gaussian 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)