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: "an evaluation of naive bayesian anti spam filtering"
  Abstract: It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited bulk e-mail (“spam”). We conduct a thorough evaluation of this proposal on a corpus that we make publicly available, contributing towards standard benchmarks. At the same time we investigate the effect of attribute-set size, training-corpus size, lemmatization, and stop-lists on the filter’s performance, issues that had not been previously explored. After introducing appropriate cost-sensitive evaluation measures, we reach the conclusion that additional safety nets are needed for the Naive Bayesian anti-spam filter to be viable in practice.

Connected through hyperedge HE-1 (induced by source paper "high dimensional vector semantics"):
  with members:
    - "approximation with random bases"
    - "a primer on neural network models for natural language processing"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "modelling cyber security experts decision making processes using aggregation operators"):
  with members:
    - "privileged information for data clustering"
    - "towards a more systematic approach to secure systems design and analysis"
    - "real time alert correlation with type graphs"
    - "information fusion in the immune system"
    - "immune system approaches to intrusion detection a review"

Connected through hyperedge HE-3 (induced by source paper "pretzel email encryption and provider supplied functions are compatible"):
  with members:
    - "stealing machine learning models via prediction apis"
    - "evasion attacks against machine learning at test time"
    - "privacy preserving spam filtering"

Connected through hyperedge HE-4 (induced by source paper "robust feature selection by mutual information distributions"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "distribution of mutual information"

Connected through hyperedge HE-5 (induced by source paper "revealing social networks of spammers through spectral clustering"):
  with members:
    - "a tutorial on spectral clustering"
    - "boosting trees for anti spam email filtering"

Connected through hyperedge HE-6 (induced by source paper "a suffix tree approach to email filtering"):
  with members:
    - "machine learning in automated text categorization"

Connected through hyperedge HE-7 (induced by source paper "distribution of mutual information from complete and incomplete data"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "robust feature selection by mutual information distributions"
    - "robust inference of trees"
    - "bayesian treatment of incomplete discrete data applied to mutual information and feature selection"
    - "distribution of mutual information"

Connected through hyperedge HE-8 (induced by source paper "in vivo spam filtering a challenge problem for data mining"):
  with members:
    - "an experimental comparison of naive bayesian and keyword based anti spam filtering with personal e mail messages"
    - "robust classification for imprecise environments"

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.CL(Computation and Language)