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: "secure network code for adaptive and active attacks with no randomness in intermediate nodes"
  Abstract: We analyze the security for network code when the eavesdropper can contaminate the information on the attacked edges (active attack) and can choose the attacked edges adaptively (adaptive attack). We show that active and adaptive attacks cannot improve the performance of the eavesdropper when the code is linear. Further, we give an non-linear example, in which an adaptive attack improves the performance of the eavesdropper. We derive the capacity for the unicast case and the capacity region for the multicast case or the multiple multicast case in several examples of relay networks, beyond the minimum cut theorem, when no additional random number is allowed as scramble variables in the intermediate nodes.

Connected through hyperedge HE-1 (induced by source paper "on secure capacity of multiple unicast traffic over separable networks"):
  with members:
    - "on secure network coding for multiple unicast traffic"

Connected through hyperedge HE-2 (induced by source paper "secrecy and robustness for active attack in secure network coding and its application to network quantum key distribution"):
  with members:
    - "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"
    - "non asymptotic and asymptotic analyses of information processing on markov chains"

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)