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: "reasoning with sarcasm by reading in between"
  Abstract: Sarcasm is a sophisticated speech act which commonly manifests on social communities such as Twitter and Reddit. The prevalence of sarcasm on the social web is highly disruptive to opinion mining systems due to not only its tendency of polarity flipping but also usage of figurative language. Sarcasm commonly manifests with a contrastive theme either between positive-negative sentiments or between literal-figurative scenarios. In this paper, we revisit the notion of modeling contrast in order to reason with sarcasm. More specifically, we propose an attention-based neural model that looks in-between instead of across, enabling it to explicitly model contrast and incongruity. We conduct extensive experiments on six benchmark datasets from Twitter, Reddit and the Internet Argument Corpus. Our proposed model not only achieves state-of-the-art performance on all datasets but also enjoys improved interpretability.

Connected through hyperedge HE-1 (induced by source paper "exploring author context for detecting intended vs perceived sarcasm"):
  with members:
    - "cascade contextual sarcasm detection in online discussion forums"
    - "opennmt open source toolkit for neural machine translation"
    - "are word embedding based features useful for sarcasm detection"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "effective approaches to attention based neural machine translation"
    - "distributed representations of sentences and documents"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (induced by source paper "self attentive neural collaborative filtering"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "outer product based neural collaborative filtering"
    - "multi pointer co attention networks for recommendation"
    - "couplenet paying attention to couples with coupled attention for relationship recommendation"
    - "neural brane neural bayesian personalized ranking for attributed network embedding"
    - "personalized top n sequential recommendation via convolutional sequence embedding"

Connected through hyperedge HE-3 (induced by source paper "representing social media users for sarcasm detection"):
  with members:
    - "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "a large self annotated corpus for sarcasm"
    - "advances in pre training distributed word representations"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "exploiting social network structure for person to person sentiment analysis"

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)