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: "stealthy secret key generation"
  Abstract: In this work, we consider a complete covert communication system, which includes the source-model of a stealthy secret key generation (SSKG) as the first phase. The generated key will be used for the covert communication in the second phase of the current round and also in the first phase of the next round. We investigate the stealthy SK rate performance of the first phase. The derived results show that the SK capacity lower and upper bounds of the source-model SKG are not affected by the additional stealth constraint. This result implies that we can attain the SSKG capacity for free when the sequences observed by the three terminals Alice ($X^n$), Bob ($Y^n$) and Willie ($Z^n$) follow a Markov chain relationship, i.e., $X^n-Y^n-Z^n$. We then prove that the sufficient condition to attain both, the SK capacity as well as the SSK capacity, can be relaxed from physical to stochastic degradedness. In order to underline the practical relevance, we also derive a sufficient condition to attain the degradedness by the usual stochastic order for Maurer's fast fading Gaussian (satellite) model for the source of common randomness.

Connected through hyperedge HE-1 (induced by source paper "keyless covert communication via channel state information"):
  with members:
    - "first and second order asymptotics in covert communication"
    - "wiretap channels with causal state information strong secrecy"
    - "covert communication with channel state information at the transmitter"
    - "fundamental limits of communication with low probability of detection"
    - "the likelihood encoder for lossy compression"
    - "wiretap channels with random states non causally available at the encoder"
    - "covert communication over noisy channels a resolvability perspective"
    - "strong secrecy for cooperative broadcast channels"

Connected through hyperedge HE-2 (induced by source paper "covert secret key generation with an active warden"):
  with members:
    - "covert communication over adversarially jammed channels"
    - "covert communication in fading channels under channel uncertainty"
    - "covert communication with channel state information at the transmitter"
    - "fundamental limits of communication with low probability of detection"
    - "the likelihood encoder for lossy compression"
    - "covert communication in the presence of an uninformed jammer"
    - "covert communication over noisy channels a resolvability perspective"
    - "principles of physical layer security in multiuser wireless networks a survey"

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)