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: "wireless channel modeling perspectives for ultra reliable low latency communications"
  Abstract: Ultra-Reliable Low Latency Communication (URLLC) is one of the distinctive features of the upcoming 5G wireless communication, going down to packet error rates (PER) of $10^{-9}$. In this paper we discuss the statistical properties of the wireless channel models that are relevant for characterization of the lower tail of the Cumulative Distribution Function (CDF). We show that, for a wide range of channel models, the outage probability at URLLC levels can be approximated by a simple power law expression, whose exponent and offset depend on the actual channel model. The main insights from the analysis can be summarized as follows: (1) the two-wave model and the impact of shadowing in combined models lead to pessimistic predictions of the fading in the URLLC region; (2) the CDFs of models that contain single cluster diffuse components have slopes that correspond to the slope of a Rayleigh fading, and (3) multi-cluster diffuse components can result in different slopes. We apply our power law approximation results to analyze the performance of receiver diversity schemes for URLLC-relevant statistics and obtain a new simplified expression for Maximum Ratio Combining (MRC) in channels with power law tail statistics.

Connected through hyperedge HE-1 (induced by source paper "wireless access in ultra reliable low latency communication urllc"):
  with members:
    - "how reliable and capable is multi connectivity"
    - "wireless channel dynamics and robustness for ultra reliable low latency communications"
    - "improving network availability of ultra reliable and low latency communications with multi connectivity"
    - "packet duplication in dual connectivity enabled 5g wireless networks overview and challenges"
    - "massive mimo for ultra reliable communications with constellations for dual coherent noncoherent detection"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "energy latency tradeoff in ultra reliable low latency communication with retransmissions"
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"

Connected through hyperedge HE-2 (induced by source paper "a statistical learning approach to ultra reliable low latency communication"):
  with members:
    - "wireless channel dynamics and robustness for ultra reliable low latency communications"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "a very brief introduction to machine learning with applications to communication systems"
    - "wireless access in ultra reliable low latency communication urllc"
    - "the meta distribution of the sir in poisson bipolar and cellular networks"
    - "ultra reliable communication in 5g 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)