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: "full rate full diversity finite feedback space time schemes with minimum feedback and transmission duration"
  Abstract: In this paper a MIMO quasi static block fading channel with finite N-ary delay-free, noise-free feedback is considered. The transmitter uses a set of N Space-Time Block Codes (STBCs), one corresponding to each of the N possible feedback values, to encode and transmit information. The feedback function used at the receiver and the N component STBCs used at the transmitter together constitute a Finite Feedback Scheme (FFS). Although a number of FFSs are available in the literature that provably achieve full-diversity, there is no known universal criterion to determine whether a given arbitrary FFS achieves full-diversity or not. Further, all known full-diversity FFSs for T<N_t where N_t is the number of transmit antennas, have rate at the most 1. In this paper a universal necessary condition for any FFS to achieve full-diversity is given, using which the notion of Feedback-Transmission duration optimal (FT-Optimal) FFSs - schemes that use minimum amount of feedback N given the transmission duration T, and minimum transmission duration given the amount of feedback to achieve full-diversity - is introduced. When there is no feedback (N=1) an FT-optimal scheme consists of a single STBC with T=N_t, and the universal necessary condition reduces to the well known necessary and sufficient condition for an STBC to achieve full-diversity: every non-zero codeword difference matrix of the STBC must be of rank N_t. Also, a sufficient condition for full-diversity is given for the FFSs in which the component STBC with the largest minimum Euclidean distance is chosen. Using this sufficient condition full-rate (rate N_t) full-diversity FT-Optimal schemes are constructed for all (N_t,T,N) with NT=N_t. These are the first full-rate full-diversity FFSs reported in the literature for T<N_t. Simulation results show that the new schemes have the best error performance among all known FFSs.

Connected through hyperedge HE-1 (induced by source paper "interleaving channel estimation and limited feedback for point to point systems with a large number of transmit antennas"):
  with members:
    - "interleaved training and training based transmission design for hybrid massive antenna downlink"
    - "compressed channel feedback for correlated massive mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "antenna grouping based feedback compression for fdd based massive mimo systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "training sequence design for feedback assisted hybrid beamforming in massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"

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)