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: "sequential defense against random and intentional attacks in complex networks"
  Abstract: Network robustness against attacks is one of the most fundamental researches in network science as it is closely associated with the reliability and functionality of various networking paradigms. However, despite the study on intrinsic topological vulnerabilities to node removals, little is known on the network robustness when network defense mechanisms are implemented, especially for networked engineering systems equipped with detection capabilities. In this paper, a sequential defense mechanism is firstly proposed in complex networks for attack inference and vulnerability assessment, where the data fusion center sequentially infers the presence of an attack based on the binary attack status reported from the nodes in the network. The network robustness is evaluated in terms of the ability to identify the attack prior to network disruption under two major attack schemes, i.e., random and intentional attacks. We provide a parametric plug-in model for performance evaluation on the proposed mechanism and validate its effectiveness and reliability via canonical complex network models and real-world large-scale network topology. The results show that the sequential defense mechanism greatly improves the network robustness and mitigates the possibility of network disruption by acquiring limited attack status information from a small subset of nodes in the network.

Connected through hyperedge HE-1 (induced by source paper "measuring network resilience through connection patterns"):
  with members:
    - "resilience of core periphery networks in the case of rich club"

Connected through hyperedge HE-2 (induced by source paper "assessing the risk of advanced persistent threats"):
  with members:
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "distributed interaction between computer virus and patch a modeling study"
    - "analysis of the effectiveness of the truth spreading strategy for inhibiting rumors"
    - "on the effectiveness of the truth spreading rumor blocking strategy for restraining rumors"
    - "a discount strategy in word of mouth marketing and its assessment"
    - "a stochastic model of active cyber defense dynamics"
    - "push and pull based epidemic spreading in networks thresholds and deeper insights"
    - "optimizing active cyber defense"

Connected through hyperedge HE-3 (induced by source paper "traffic aware patching for cyber security in mobile iot"):
  with members:
    - "decapitation via digital epidemics a bio inspired transmissive attack"
    - "encounter based worms analysis and defense"

Connected through hyperedge HE-4 (induced by source paper "ecology based dos attack in cognitive radio networks"):
  with members:
    - "towards a systematic view on cybersecurity ecology"
    - "byzantine attack and defense in cognitive radio networks a survey"
    - "security a perpetual war lessons from nature"

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.SI(Social and Information Networks)