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: "channel state prediction feedback and scheduling for a multiuser mimo ofdm downlink"
  Abstract: We consider the downlink of a MIMO-OFDM wireless systems where the base-station (BS) has M antennas and serves K single-antenna user terminals (UT) with K larger than or equal to M. Users estimate their channel vectors from common downlink pilot symbols and feed back a prediction, which is used by the BS to compute the linear beamforming matrix for the next time slot and to select the users to be served according to the proportional fair scheduling (PFS) algorithm. We consider a realistic physical channel model used as a benchmark in standardization and some alternatives for the channel estimation and prediction scheme. We show that a parametric method based on ESPRIT is able to accurately predict the channel even for relatively high user mobility. However, there exists a class of channels characterized by large Doppler spread (high mobility) and clustered angular spread for which prediction is intrinsically difficult and all considered methods fail. We propose a modified PFS that take into account the "predictability" state of the UTs, and significantly outperform the classical PFS in the presence of prediction errors. The main conclusion of this work is that multiuser MIMO downlink yields very good performance even in the presence of high mobility users, provided that the nonpredictable users are handled appropriately

Connected through hyperedge HE-1 (induced by source paper "indoor massive mimo uplink pilot mitigation using channel state information map"):
  with members:
    - "selective uplink training for massive mimo systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "massive mimo how many antennas do we need"

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)