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: "correction factor for analysis of mimo wireless networks with highly directional beamforming"
  Abstract: Most theoretical studies for multiple input multiple output (MIMO) wireless networks with beamforming model the actual received signal power as a product of some fixed antenna gain and the nominal received signal power. The nominal received signal power being that from an omni-directional transmission on a single input single output (SISO) link. Since the power angular spectrum is often significantly different for line of sight (LOS) and non-LOS (NLOS) links, multiplying the same antenna gain for both LOS and NLOS links leads to erroneous estimates of the actual received signal power. We quantify the difference in effective antenna gain for NLOS and LOS links as a function of number of dominant paths in the channel using a popular narrowband geometric channel model, when the number of antennas at the transmitter and receiver is large. We call this difference the correction factor. Using the recent New Radio (NR) 3GPP channel model we confirm that the effective antenna gain for NLOS links can be about 10 dB lower than for LOS links.

Connected through hyperedge HE-1 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave networks"):
  with members:
    - "path selection and rate allocation in self backhauled mmwave networks"
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "energy efficient interactive beam alignment for millimeter wave networks"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "toward low latency and ultra reliable virtual reality"
    - "millimeter wave v2v communications distributed association and beam alignment"
    - "capacity scaling of cellular networks impact of bandwidth infrastructure density and number of antennas"
    - "joint load balancing and interference mitigation in 5g heterogeneous networks"

Connected through hyperedge HE-2 (induced by source paper "stochastic geometric coverage analysis in mmwave cellular networks with realistic channel and antenna radiation models"):
  with members:
    - "study of realistic antenna patterns in 5g mmwave cellular scenarios"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "coverage and connectivity analysis of millimeter wave vehicular networks"
    - "millimeter wave interference avoidance via building aware associations"
    - "modeling and analyzing millimeter wave cellular systems"
    - "coverage analysis for millimeter wave networks the impact of directional antenna arrays"
    - "stochastic geometric coverage analysis in mmwave cellular networks with a realistic channel model"
    - "revisiting frequency reuse towards supporting ultra reliable ubiquitous rate communication"

Connected through hyperedge HE-3 (induced by source paper "max min rates in self backhauled millimeter wave cellular networks"):
  with members:
    - "improving the coverage and spectral efficiency of millimeter wave cellular networks using device to device relays"
    - "modeling and analyzing millimeter wave cellular systems"
    - "inter operator resource management for millimeter wave multi hop backhaul networks"
    - "performance of dynamic and static tdd in self backhauled millimeter wave cellular networks"
    - "optimal joint routing and scheduling in millimeter wave cellular networks"
    - "gbps user rates using mmwave relayed backhaul with high gain antennas"
    - "mmwave vehicle to infrastructure communication analysis of urban microcellular networks"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"

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)