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: "universal strongly secure network coding with dependent and non uniform messages"
  Abstract: We consider the random linear precoder at the source node as a secure network coding. We prove that it is strongly secure in the sense of Harada and Yamamoto and universal secure in the sense of Silva and Kschischang, while allowing arbitrary small but nonzero mutual information to the eavesdropper. Our security proof allows statistically dependent and non-uniform multiple secret messages, while all previous constructions of weakly or strongly secure network coding assumed independent and uniform messages, which are difficult to be ensured in practice.

Connected through hyperedge HE-1 (induced by source paper "secrecy and robustness for active attack in secure network coding and its application to network quantum key distribution"):
  with members:
    - "secure network code for adaptive and active attacks with no randomness in intermediate nodes"
    - "universal construction of cheater identifiable secret sharing against rushing cheaters without honest majority"
    - "universal secure multiplex network coding with dependent and non uniform messages"
    - "secure multi source multicast"
    - "quantum inspired secure wireless communication protocol under spatial and local gaussian noise assumptions"
    - "secure wireless communication under spatial and local gaussian noise assumptions"
    - "relative generalized rank weight of linear codes and its applications to network coding"
    - "coding against a limited view adversary the effect of causality and feedback"

Connected through hyperedge HE-2 (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"
    - "universally attainable error and information exponents and equivocation rate for the broadcast channels with confidential 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-3 (induced by source paper "uniform random number generation from markov chains non asymptotic and asymptotic analyses"):
  with members:
    - "security analysis of varepsilon almost dual universal 2 hash functions smoothing of min entropy versus smoothing of renyi entropy of order 2"
    - "second order coding rates for channels with state"
    - "non asymptotic and asymptotic analyses of information processing on markov chains"
    - "second order resolvability intrinsic randomness and fixed length source coding for mixed sources information spectrum approach"
    - "tight exponential analysis of universally composable privacy amplification and its applications"
    - "non asymptotic analysis of privacy amplification via renyi entropy and inf spectral entropy"
    - "exponential decreasing rate of leaked information in universal random privacy amplification"
    - "moderate deviations of lossy source coding for discrete and gaussian sources"

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)