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: "a joint model of conversational discourse and latent topics on microblogs"
  Abstract: Conventional topic models are ineffective for topic extraction from microblog messages, because the data sparseness exhibited in short messages lacking structure and contexts results in poor message-level word co-occurrence patterns. To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: 1) different roles of conversational discourse, 2) various latent topics in reflecting content information. By explicitly distinguishing the probabilities of messages with varying discourse roles in containing topical words, our model is able to discover clusters of discourse words that are indicative of topical content. In an automatic evaluation on large-scale microblog corpora, our joint model yields topics with better coherence scores than competitive topic models from previous studies. Qualitative analysis on model outputs indicates that our model induces meaningful representations for both discourse and topics. We further present an empirical study on microblog summarization based on the outputs of our joint model. The results show that the jointly modeled discourse and topic representations can effectively indicate summary-worthy content in microblog conversations.

Connected through hyperedge HE-1 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process"):
  with members:
    - "unsupervised dialog structure learning"
    - "what you say and how you say it joint modeling of topics and discourse in microblog conversations"
    - "what gets echoed understanding the pointers in explanations of persuasive arguments"
    - "topic memory networks for short text classification"
    - "attentive interaction model modeling changes in view in argumentation"
    - "joint modeling of content and discourse relations in dialogues"
    - "winning on the merits the joint effects of content and style on debate outcomes"
    - "argument strength is in the eye of the beholder audience effects in persuasion"

Connected through hyperedge HE-2 (induced by source paper "conversational structure aware and context sensitive topic model for online discussions"):
  with members:
    - "context aware image annotation in active learning"
    - "eliminating search intent bias in learning to rank"
    - "knowledge guided text structuring in clinical trials"
    - "improving topic models with latent feature word representations"
    - "jointly learning word embeddings and latent topics"
    - "event detection tracking and visualization in twitter a mention anomaly based approach"
    - "probabilistic latent semantic analysis"

Connected through hyperedge HE-3 (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"
    - "joint modeling of content and discourse relations in dialogues"
    - "find the conversation killers a predictive study of thread ending posts"
    - "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-4 (induced by source paper "topic aware neural keyphrase generation for social media language"):
  with members:
    - "an integrated approach for keyphrase generation via exploring the power of retrieval and extraction"
    - "microblog hashtag generation via encoding conversation contexts"
    - "what you say and how you say it joint modeling of topics and discourse in microblog conversations"
    - "keyphrase generation with correlation constraints"
    - "topic memory networks for short text classification"
    - "title guided encoding for keyphrase generation"
    - "deep keyphrase generation"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-5 (induced by source paper "microblog hashtag generation via encoding conversation contexts"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "semi supervised learning for neural keyphrase generation"
    - "title guided encoding for keyphrase generation"
    - "opennmt open source toolkit for neural machine translation"
    - "deep keyphrase generation"
    - "bidirectional attention flow for machine comprehension"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "end to end memory 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)