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: "mining user comment activity for detecting forum spammers in youtube"
  Abstract: Research shows that comment spamming (comments which are unsolicited, unrelated, abusive, hateful, commercial advertisements etc) in online discussion forums has become a common phenomenon in Web 2.0 applications and there is a strong need to counter or combat comment spamming. We present a method to automatically detect comment spammer in YouTube (largest and a popular video sharing website) forums. The proposed technique is based on mining comment activity log of a user and extracting patterns (such as time interval between subsequent comments, presence of exactly same comment across multiple unrelated videos) indicating spam behavior. We perform empirical analysis on data crawled from YouTube and demonstrate that the proposed method is effective for the task of comment spammer detection.

Connected through hyperedge HE-1 (induced by source paper "disturbed youtube for kids characterizing and detecting inappropriate videos targeting young children"):
  with members:
    - "combating the elsagate phenomenon deep learning architectures for disturbing cartoons"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "characterizing videos audience and advertising in youtube channels for kids"
    - "kidstube detection characterization and analysis of child unsafe content promoters on youtube"
    - "tensorflow a system for large scale machine learning"
    - "going deeper with convolutions"
    - "network analysis of recurring youtube spam campaigns"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-2 (induced by source paper "trollspot detecting misbehavior in commenting platforms"):
  with members:
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "antisocial behavior in online discussion communities"

Connected through hyperedge HE-3 (induced by source paper "kidstube detection characterization and analysis of child unsafe content promoters on youtube"):
  with members:
    - "network analysis of recurring youtube spam campaigns"

Connected through hyperedge HE-4 (induced by source paper "identifying discriminating network motifs in youtube spam"):
  with members:
    - "network analysis of recurring youtube spam campaigns"

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.IR(Information Retrieval)