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 hardening in massive mimo a measurement based analysis"
  Abstract: Wireless-controlled robots, cars and other critical applications are in need of technologies that offer high reliability and low latency. Massive MIMO, Multiple-Input Multiple-Output, is a key technology for the upcoming 5G systems and is one part of the solution to increase the reliability of wireless systems. More specifically, when increasing the number of base station antennas in a massive MIMO systems the channel variations decrease and the so-called channel hardening effect appears. This means that the variations of the channel gain in time and frequency decrease. In this paper, channel hardening in massive MIMO systems is assessed based on analysis of measurement data. For an indoor scenario, the channels are measured with a 128-port cylindrical array for nine single-antenna users. The analysis shows that in a real scenario a channel hardening of 3.2-4.6 dB, measured as a reduction of the standard deviation of the channel gain, can be expected depending on the amount of user interaction. Also, some practical implications and insights are presented.

Connected through hyperedge HE-1 (induced by source paper "massive mimo asymptotics for ray based propagation channels"):
  with members:
    - "mimo channel hardening a physical model based analysis"
    - "energy efficiency and asymptotic performance evaluation of beamforming structures in doubly massive mimo mmwave systems"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "uplink analysis of large mu mimo systems with space constrained arrays in ricean fading"
    - "no downlink pilots are needed in tdd massive mimo"
    - "massive mimo ten myths and one critical question"
    - "asymptotic orthogonality analysis of time domain sparse massive mimo channels"
    - "massive mimo performance evaluation based on measured propagation data"

Connected through hyperedge HE-2 (induced by source paper "efficient pilot allocation for urllc traffic in 5g industrial iot networks"):
  with members:
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "random access for massive mimo systems with intra cell pilot contamination"
    - "random access protocol for massive mimo strongest user collision resolution sucr"
    - "massive mimo for crowd scenarios a solution based on random access"
    - "massive mimo for next generation wireless systems"

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)