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: "analyzing worms and network traffic using compression"
  Abstract: Internet worms have become a widespread threat to system and network operations. In order to fight them more efficiently, it is necessary to analyze newly discovered worms and attack patterns. This paper shows how techniques based on Kolmogorov Complexity can help in the analysis of internet worms and network traffic. Using compression, different species of worms can be clustered by type. This allows us to determine whether an unknown worm binary could in fact be a later version of an existing worm in an extremely simple, automated, manner. This may become a useful tool in the initial analysis of malicious binaries. Furthermore, compression can also be useful to distinguish different types of network traffic and can thus help to detect traffic anomalies: Certain anomalies may be detected by looking at the compressibility of a network session alone. We furthermore show how to use compression to detect malicious network sessions that are very similar to known intrusion attempts. This technique could become a useful tool to detect new variations of an attack and thus help to prevent IDS evasion. We provide two new plugins for Snort which demonstrate both approaches.

Connected through hyperedge HE-1 (induced by source paper "identifying cover songs using information theoretic measures of similarity"):
  with members:
    - "blind construction of optimal nonlinear recursive predictors for discrete sequences"
    - "shannon information and kolmogorov complexity"
    - "on prediction using variable order markov models"
    - "the similarity metric"

Connected through hyperedge HE-2 (induced by source paper "on normalized compression distance and large malware"):
  with members:
    - "the similarity metric"

Connected through hyperedge HE-3 (induced by source paper "detecting malware with information complexity"):
  with members:
    - "the similarity metric"

Connected through hyperedge HE-4 (induced by source paper "a fast quartet tree heuristic for hierarchical clustering"):
  with members:
    - "the google similarity distance"
    - "the similarity metric"
    - "a discipline of evolutionary programming"

Connected through hyperedge HE-5 (induced by source paper "signature generation for sensitive information leakage in android applications"):
  with members:
    - "adsplit separating smartphone advertising from applications"

Connected through hyperedge HE-6 (induced by source paper "analysis and study on text representation to improve the accuracy of the normalized compression distance"):
  with members:
    - "evaluating the impact of information distortion on normalized compression distance"
    - "the google similarity distance"
    - "the similarity metric"

Connected through hyperedge HE-7 (induced by source paper "information distance in multiples"):
  with members:
    - "the google similarity distance"
    - "the similarity metric"

Connected through hyperedge HE-8 (induced by source paper "similarity of objects and the meaning of words"):
  with members:
    - "a fast quartet tree heuristic for hierarchical clustering"
    - "the similarity metric"

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)