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: "covert capacity of non coherent rayleigh fading channels"
  Abstract: The covert capacity is characterized for a non-coherent fast Rayleigh-fading wireless channel, in which a legitimate user wishes to communicate reliably with a legitimate receiver while escaping detection from a warden. It is shown that the covert capacity is achieved with an amplitude-constrained input distribution that consists of a finite number of mass points including one at zero and numerically tractable bounds are provided. It is also conjectured that distributions with two mass points in fixed locations are optimal.

Connected through hyperedge HE-1 (induced by source paper "fundamental limits of covert packet insertion"):
  with members:
    - "covert communication over a k user multiple access channel"
    - "fundamental limits of invisible flow fingerprinting"
    - "fundamental limits of covert bit insertion in packets"
    - "privacy preserving mhealth data release with pattern consistency"
    - "towards provably invisible network flow fingerprints"
    - "covert wireless communication with artificial noise generation"
    - "covert communication over noisy channels a resolvability perspective"
    - "covert communications on renewal packet channels"

Connected through hyperedge HE-2 (induced by source paper "throughput scaling of covert communication over wireless adhoc networks"):
  with members:
    - "fundamental limits of covert communication over mimo awgn channel"
    - "time division is optimal for covert communication over some broadcast channels"
    - "covert communication with channel state information at the transmitter"
    - "fundamental limits of communication with low probability of detection"
    - "covert communication over noisy channels a resolvability perspective"
    - "limits of reliable communication with low probability of detection on awgn channels"
    - "cognitive networks achieve throughput scaling of a homogeneous network"
    - "on capacity scaling in arbitrary wireless 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)