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: "learning latent local conversation modes for predicting community endorsement in online discussions"
  Abstract: Many social media platforms offer a mechanism for readers to react to comments, both positively and negatively, which in aggregate can be thought of as community endorsement. This paper addresses the problem of predicting community endorsement in online discussions, leveraging both the participant response structure and the text of the comment. The different types of features are integrated in a neural network that uses a novel architecture to learn latent modes of discussion structure that perform as well as deep neural networks but are more interpretable. In addition, the latent modes can be used to weight text features thereby improving prediction accuracy.

Connected through hyperedge HE-1 (induced by source paper "the anatomy of reddit an overview of academic research"):
  with members:
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "the aftermath of disbanding an online hateful community"
    - "tracing community genealogy how new communities emerge from the old"
    - "community interaction and conflict on the web"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "on the origins of memes by means of fringe web communities"
    - "conversation modeling on reddit using a graph structured lstm"
    - "identifying the social signals that drive online discussions a case study of reddit communities"

Connected through hyperedge HE-2 (induced by source paper "exploring the role of prior beliefs for argument persuasion"):
  with members:
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "argument mining with structured svms and rnns"
    - "conversational flow in oxford style debates"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"

Connected through hyperedge HE-3 (induced by source paper "conversation modeling on reddit using a graph structured lstm"):
  with members:
    - "deep reinforcement learning with a combinatorial action space for predicting popular reddit threads"
    - "recurrent neural network grammars"
    - "theano a python framework for fast computation of mathematical expressions"
    - "compositional distributional semantics with long short term memory"
    - "improved semantic representations from tree structured long short term memory networks"
    - "top down tree long short term memory networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "talking to the crowd what do people react to in online discussions"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning with external knowledge and two stage q functions for predicting popular reddit threads"):
  with members:
    - "deep reinforcement learning for dialogue generation"
    - "a network based end to end trainable task oriented dialogue system"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "improving information extraction by acquiring external evidence with reinforcement learning"
    - "end to end goal driven web navigation"
    - "end to end memory networks"
    - "deep reinforcement learning in large discrete action spaces"
    - "deep reinforcement learning with a natural language action space"

Connected through hyperedge HE-5 (induced by source paper "argument strength is in the eye of the beholder audience effects in persuasion"):
  with members:
    - "getting reliable annotations for sarcasm in online dialogues"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "learning fine grained knowledge about contingent relations between everyday events"
    - "measuring the similarity of sentential arguments in dialogue"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "and that s a fact distinguishing factual and emotional argumentation in online dialogue"

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.SI(Social and Information Networks)