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: "find the conversation killers a predictive study of thread ending posts"
  Abstract: How to improve the quality of conversations in online communities has attracted considerable attention recently. Having engaged, urbane, and reactive online conversations has a critical effect on the social life of Internet users. In this study, we are particularly interested in identifying a post in a multi-party conversation that is unlikely to be further replied to, which therefore kills that thread of the conversation. For this purpose, we propose a deep learning model called the ConverNet. ConverNet is attractive due to its capability of modeling the internal structure of a long conversation and its appropriate encoding of the contextual information of the conversation, through effective integration of attention mechanisms. Empirical experiments on real-world datasets demonstrate the effectiveness of the proposal model. For the widely concerned topic, our analysis also offers implications for improving the quality and user experience of online conversations.

Connected through hyperedge HE-1 (induced by source paper "what you say and how you say it joint modeling of topics and discourse in microblog conversations"):
  with members:
    - "improving topic models with latent feature word representations"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "topic memory networks for short text classification"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "joint modeling of content and discourse relations in dialogues"
    - "discovering discrete latent topics with neural variational inference"
    - "jointly learning word embeddings and latent topics"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "joint effects of context and user history for predicting online conversation re entries"):
  with members:
    - "conversations gone awry detecting early signs of conversational failure"
    - "conversation modeling on reddit using a graph structured lstm"
    - "bidirectional attention flow for machine comprehension"
    - "end to end memory networks"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "characterizing and curating conversation threads expansion focus volume re entry"

Connected through hyperedge HE-3 (induced by source paper "when to reply context sensitive models to predict instructor interventions in mooc forums"):
  with members:
    - "conversation modeling on reddit using a graph structured lstm"
    - "using discourse signals for robust instructor intervention prediction"
    - "learning instructor intervention from mooc forums early results and issues"
    - "neural machine translation by jointly learning to align and translate"

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)