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: "push and pull based epidemic spreading in networks thresholds and deeper insights"
  Abstract: Understanding the dynamics of computer virus (malware, worm) in cyberspace is an important problem that has attracted a fair amount of attention. Early investigations for this purpose adapted biological epidemic models, and thus inherited the so-called homogeneity assumption that each node is equally connected to others. Later studies relaxed this often-unrealistic homogeneity assumption, but still focused on certain power-law networks. Recently, researchers investigated epidemic models in {\em arbitrary} networks (i.e., no restrictions on network topology). However, all these models only capture {\em push-based} infection, namely that an infectious node always actively attempts to infect its neighboring nodes. Very recently, the concept of {\em pull-based} infection was introduced but was not treated rigorously. Along this line of research, the present paper investigates push- and pull-based epidemic spreading dynamics in arbitrary networks, using a Non-linear Dynamical Systems approach. The paper advances the state of the art as follows: (1) It presents a more general and powerful sufficient condition (also known as epidemic threshold in the literature) under which the spreading will become stable. (2) It gives both upper and lower bounds on the global mean infection rate, regardless of the stability of the spreading. (3) It offers insights into, among other things, the estimation of the global mean infection rate through localized monitoring of a small {\em constant} number of nodes, {\em without} knowing the values of the parameters.

Connected through hyperedge HE-1 (induced by source paper "metrics towards measuring cyber agility"):
  with members:
    - "characterizing honeypot captured cyber attacks statistical framework and case study"
    - "a stochastic model of active cyber defense dynamics"
    - "optimizing active cyber defense"
    - "an extended stochastic model for quantitative security analysis of networked systems"
    - "predicting cyber attack rates with extreme values"
    - "cyber epidemic models with dependences"
    - "a new approach to modeling and analyzing security of networked systems"
    - "active cyber defense dynamics exhibiting rich phenomena"

Connected through hyperedge HE-2 (induced by source paper "node diversification in complex networks by decentralized colouring"):
  with members:
    - "active cyber defense dynamics exhibiting rich phenomena"
    - "preventive and reactive cyber defense dynamics is globally stable"
    - "distributed deterministic edge coloring using bounded neighborhood independence"

Connected through hyperedge HE-3 (induced by source paper "an effective rumor containing strategy"):
  with members:
    - "a stochastic model of active cyber defense dynamics"

Connected through hyperedge HE-4 (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"
    - "optimizing active cyber defense"
    - "epidemic threshold of an sis model in dynamic switching networks"

Connected through hyperedge HE-5 (induced by source paper "security evaluation of cyber networks under advanced persistent threats"):
  with members:
    - "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"
    - "optimizing active cyber defense"
    - "preventive and reactive cyber defense dynamics is globally stable"

Connected through hyperedge HE-6 (induced by source paper "defend against advanced persistent threats an optimal control approach"):
  with members:
    - "a stochastic model of active cyber defense dynamics"
    - "preventive and reactive cyber defense dynamics is globally stable"

Connected through hyperedge HE-7 (induced by source paper "optimizing top precision performance measure of content based image retrieval by learning similarity function"):
  with members:
    - "supervised learning of sparse context reconstruction coefficients for data representation and classification"
    - "representing data by sparse combination of contextual data points for classification"
    - "remote health coaching system and human motion data analysis for physical therapy with microsoft kinect"
    - "evaluation of pose tracking accuracy in the first and second generations of microsoft kinect"
    - "image tag completion by local learning"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "unsupervised temporal segmentation of repetitive human actions based on kinematic modeling and frequency analysis"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"

Connected through hyperedge HE-8 (induced by source paper "stochastic learning of multi instance dictionary for earth mover s distance based histogram comparison"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "sparse coding with earth mover s distance for multi instance histogram representation"
    - "supervised learning of sparse context reconstruction coefficients for data representation and classification"
    - "domain transfer multi instance dictionary learning"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"
    - "an evasion and counter evasion study in malicious websites detection"

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.CR(Cryptography and Security)