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: "universally attainable error and information exponents and equivocation rate for the broadcast channels with confidential messages"
  Abstract: We show universally attainable exponents for the decoding error and the mutual information and universally attainable equivocation rates for the conditional entropy for the broadcast channels with confidential messages. The error exponents are the same as ones given by Korner and Sgarro for the broadcast channels with degraded message sets.

Connected through hyperedge HE-1 (induced by source paper "polar codes simplicity random codes durability"):
  with members:
    - "reed muller codes polarize"
    - "ar i kan meets shannon polar codes with near optimal convergence to channel capacity"
    - "explicit polar codes with small scaling exponent"
    - "on the capacity of channels with deletions and states"
    - "polar codes for the deletion channel weak and strong polarization"
    - "polar like codes and asymptotic tradeoff among block length code rate and error probability"
    - "scaling exponent and moderate deviations asymptotics of polar codes for the awgn channel"
    - "relaxed polar codes"

Connected through hyperedge HE-2 (induced by source paper "exact exponent for soft covering"):
  with members:
    - "first and second order asymptotics in covert communication"
    - "exact random coding secrecy exponents for the wiretap channel"
    - "second order asymptotics in covert communication"
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "soft covering with high probability"
    - "a stronger soft covering lemma and applications"
    - "renyi divergence and kullback leibler divergence"
    - "exact random coding error exponents of optimal bin index decoding"

Connected through hyperedge HE-3 (induced by source paper "exact random coding secrecy exponents for the wiretap channel"):
  with members:
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "soft covering with high probability"
    - "the sender excited secret key agreement model capacity reliability and secrecy exponents"
    - "on the secrecy exponent of the wire tap channel"
    - "exact random coding error exponents of optimal bin index decoding"
    - "informational divergence approximations to product distributions"
    - "effective secrecy reliability confusion and stealth"
    - "tight exponential analysis of universally composable privacy amplification and its applications"

Connected through hyperedge HE-4 (induced by source paper "secure multiplex coding with dependent and non uniform multiple messages"):
  with members:
    - "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions"
    - "tight exponential analysis of universally composable privacy amplification and its applications"
    - "exponential decreasing rate of leaked information in universal random privacy amplification"
    - "secure multiplex coding with a common message"
    - "universal strongly secure network coding with dependent and non uniform messages"
    - "achieving the secrecy capacity of wiretap channels using polar codes"
    - "construction of wiretap codes from ordinary channel codes"
    - "information spectrum approach to second order coding rate in channel coding"

Connected through hyperedge HE-5 (induced by source paper "on the secrecy exponent of the wire tap channel"):
  with members:
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "the sender excited secret key agreement model capacity reliability and secrecy exponents"
    - "exact random coding error exponents of optimal bin index decoding"
    - "informational divergence approximations to product distributions"
    - "effective secrecy reliability confusion and stealth"
    - "reliability and secrecy functions of the wiretap channel under cost constraint"
    - "exponential decreasing rate of leaked information in universal random privacy amplification"
    - "strong secrecy from channel resolvability"

Connected through hyperedge HE-6 (induced by source paper "secure multiplex coding with a common message"):
  with members:
    - "exponential decreasing rate of leaked information in universal random privacy amplification"
    - "secure multiplex network coding"
    - "strong security and separated code constructions for the broadcast channels with confidential messages"
    - "secure multiplex coding to attain the channel capacity in wiretap channels"

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)