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: "automatic verbal aggression detection for russian and american imageboards"
  Abstract: The problem of aggression for Internet communities is rampant. Anonymous forums usually called imageboards are notorious for their aggressive and deviant behaviour even in comparison with other Internet communities. This study is aimed at studying ways of automatic detection of verbal expression of aggression for the most popular American (4chan.org) and Russian (2ch.hk) imageboards. A set of 1,802,789 messages was used for this study. The machine learning algorithm word2vec was applied to detect the state of aggression. A decent result is obtained for English (88%), the results for Russian are yet to be improved.

Connected through hyperedge HE-1 (induced by source paper "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"):
  with members:
    - "detecting state of aggression in sentences using cnn"
    - "antisocial behavior in online discussion communities"
    - "experimental analysis of popular smartphone apps offering anonymity ephemerality and end to end encryption"
    - "stfu noob predicting crowdsourced decisions on toxic behavior in online games"
    - "clustering memes in social media"

Connected through hyperedge HE-2 (induced by source paper "a longitudinal measurement study of 4chan s politically incorrect forum and its effect on the web"):
  with members:
    - "detecting state of aggression in sentences using cnn"
    - "antisocial behavior in online discussion communities"
    - "experimental analysis of popular smartphone apps offering anonymity ephemerality and end to end encryption"
    - "stfu noob predicting crowdsourced decisions on toxic behavior in online games"
    - "clustering memes in social media"

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)