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: "wiretap channel with causal state information"
  Abstract: A lower bound on the secrecy capacity of the wiretap channel with state information available causally at both the encoder and decoder is established. The lower bound is shown to be strictly larger than that for the noncausal case by Liu and Chen. Achievability is proved using block Markov coding, Shannon strategy, and key generation from common state information. The state sequence available at the end of each block is used to generate a key, which is used to enhance the transmission rate of the confidential message in the following block. An upper bound on the secrecy capacity when the state is available noncausally at the encoder and decoder is established and is shown to coincide with the lower bound for several classes of wiretap channels with state.

Connected through hyperedge HE-1 (induced by source paper "the sender excited secret key agreement model capacity reliability and secrecy exponents"):
  with members:
    - "secret key capacity for multipleaccess channel with public feedback"
    - "on the dispersions of three network information theory problems"
    - "key generation using external source excitation capacity reliability and secrecy exponent"
    - "secret key agreement with channel state information at the transmitter"
    - "exponential decreasing rate of leaked information in universal random privacy amplification"
    - "strong secrecy from channel resolvability"
    - "lecture notes on network information theory"
    - "strongly secure privacy amplification cannot be obtained by encoder of slepian wolf code"

Connected through hyperedge HE-2 (induced by source paper "wiretap channel with causal state information and secure rate limited feedback"):
  with members:
    - "mimo broadcast channel with an unknown eavesdropper secrecy degrees of freedom"
    - "physical layer security in downlink multi antenna cellular networks"
    - "on secure communication with constrained randomization"
    - "cognitive interference channels with confidential messages under randomness constraint"
    - "broadcast channels with confidential messages by randomness constrained stochastic encoder"
    - "secret key agreement with channel state information at the transmitter"
    - "secret sharing ldpc codes for the bpsk constrained gaussian wiretap channel"
    - "strong secrecy on the binary erasure wiretap channel using large girth ldpc codes"

Connected through hyperedge HE-3 (induced by source paper "on the achievable rate region of a new wiretap channel with side information"):
  with members:
    - "secret key agreement with channel state information at the transmitter"
    - "the two way wiretap channel achievable regions and experimental results"
    - "the secrecy capacity region of the gaussian mimo multi receiver wiretap channel"

Connected through hyperedge HE-4 (induced by source paper "secret key agreement with channel state information at the transmitter"):
  with members:
    - "secrecy via sources and channels"
    - "lecture notes on network information theory"
    - "3 receiver broadcast channels with common and confidential messages"
    - "fingerprints in the ether using the physical layer for wireless authentication"

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)