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: "detecting smart spammers on social network a topic model approach"
  Abstract: Spammer detection on social network is a challenging problem. The rigid anti-spam rules have resulted in emergence of "smart" spammers. They resemble legitimate users who are difficult to identify. In this paper, we present a novel spammer classification approach based on Latent Dirichlet Allocation (LDA), a topic model. Our approach extracts both the local and the global information of topic distribution patterns, which capture the essence of spamming. Tested on one benchmark dataset and one self-collected dataset, our proposed method outperforms other stateof-the-art methods in terms of averaged F1score.

Connected through hyperedge HE-1 (induced by source paper "eggs a flexible approach to relational modeling of social network spam"):
  with members:
    - "the libra toolkit for probabilistic models"
    - "hinge loss markov random fields and probabilistic soft logic"
    - "network analysis of recurring youtube spam campaigns"

Connected through hyperedge HE-2 (induced by source paper "collective classification of spam campaigners on twitter a hierarchical meta path based approach"):
  with members:
    - "poised spotting twitter spam off the beaten paths"
    - "dial one for scam a large scale analysis of technical support scams"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"
    - "towards detecting compromised accounts on social networks"
    - "detecting criminal organizations in mobile phone networks"
    - "meta path based collective classification in heterogeneous information networks"
    - "hetesim a general framework for relevance measure in heterogeneous networks"

Connected through hyperedge HE-3 (induced by source paper "poised spotting twitter spam off the beaten paths"):
  with members:
    - "mean birds detecting aggression and bullying on twitter"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"
    - "towards detecting compromised accounts on social networks"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "evasion attacks against machine learning at test time"
    - "virality prediction and community structure in social networks"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

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)