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: "secrecy and robustness for active attack in secure network coding and its application to network quantum key distribution"
  Abstract: In the network coding, we discuss the effect of sequential error injection on information leakage. We show that there is no improvement when the network is composed of linear operations. However, when the network contains non-linear operations, we find a counterexample to improve Eve's obtained information. Furthermore, we discuss the asymptotic rate in a linear network under the secrecy and robustness conditions as well as under the secrecy condition alone. Finally, we apply our results to network quantum key distribution, which clarifies the type of network that enables us to realize secure long distance communication via short distance quantum key distribution.

Connected through hyperedge HE-1 (induced by source paper "secure network code for adaptive and active attacks with no randomness in intermediate nodes"):
  with members:
    - "universal secure multiplex network coding with dependent and non uniform messages"
    - "secure multi source multicast"
    - "coding across unicast sessions can increase the secure message capacity"
    - "quantum inspired secure wireless communication protocol under spatial and local gaussian noise assumptions"
    - "secure wireless communication under spatial and local gaussian noise assumptions"
    - "on secure network coding for two unicast sessions studying butterflies"
    - "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 "quantum inspired secure wireless communication protocol under spatial and local gaussian noise assumptions"):
  with members:
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "key capacity for product sources with application to stationary gaussian processes"
    - "tight exponential analysis of universally composable privacy amplification and its applications"
    - "separation of reliability and secrecy in rate limited secret key generation"
    - "exponential decreasing rate of leaked information in universal random privacy amplification"
    - "secure transmission with artificial noise over fading channels achievable rate and optimal power allocation"
    - "information theoretically secret key generation for fading wireless channels"
    - "the secrecy capacity of the mimo wiretap channel"

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)