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: "selective uplink training for massive mimo systems"
  Abstract: As a promising technique to meet the drastically growing demand for both high throughput and uniform coverage in the fifth generation (5G) wireless networks, massive multiple-input multiple-output (MIMO) systems have attracted significant attention in recent years. However, in massive MIMO systems, as the density of mobile users (MUs) increases, conventional uplink training methods will incur prohibitively high training overhead, which is proportional to the number of MUs. In this paper, we propose a selective uplink training method for massive MIMO systems, where in each channel block only part of the MUs will send uplink pilots for channel training, and the channel states of the remaining MUs are predicted from the estimates in previous blocks, taking advantage of the channels' temporal correlation. We propose an efficient algorithm to dynamically select the MUs to be trained within each block and determine the optimal uplink training length. Simulation results show that the proposed training method provides significant throughput gains compared to the existing methods, while much lower estimation complexity is achieved. It is observed that the throughput gain becomes higher as the MU density increases.

Connected through hyperedge HE-1 (induced by source paper "channel coherence classification with frame shifting in massive mimo systems"):
  with members:
    - "massive mimo 10 myths and one grand question"
    - "pilot clustering in asymmetric massive mimo networks"
    - "smart pilot assignment for massive mimo"
    - "blind pilot decontamination"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "massive mimo how many antennas do we need"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-2 (induced by source paper "locally orthogonal training design for cloud rans based on graph coloring"):
  with members:
    - "rrh based massive mimo with on the fly pilot contamination control"
    - "cell free massive mimo versus small cells"
    - "superimposed pilots are superior for mitigating pilot contamination in massive mimo part i theory and channel estimation"
    - "fundamental limits of training based multiuser mimo systems"
    - "training design and channel estimation in uplink cloud radio access networks"
    - "joint power and antenna selection optimization in large cloud radio access networks"
    - "dynamic nested clustering for parallel phy layer processing in cloud rans"
    - "blind pilot decontamination"

Connected through hyperedge HE-3 (induced by source paper "indoor massive mimo uplink pilot mitigation using channel state information map"):
  with members:
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "massive mimo how many antennas do we need"
    - "channel state prediction feedback and scheduling for a multiuser mimo ofdm downlink"

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)