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: "two pass discourse segmentation with pairing and global features"
  Abstract: Previous attempts at RST-style discourse segmentation typically adopt features centered on a single token to predict whether to insert a boundary before that token. In contrast, we develop a discourse segmenter utilizing a set of pairing features, which are centered on a pair of adjacent tokens in the sentence, by equally taking into account the information from both tokens. Moreover, we propose a novel set of global features, which encode characteristics of the segmentation as a whole, once we have an initial segmentation. We show that both the pairing and global features are useful on their own, and their combination achieved an F1 of 92.6% of identifying insentence discourse boundaries, which is a 17.8% error-rate reduction over the stateof-the-art performance, approaching 95% of human performance. In addition, similar improvement is observed across different classification frameworks.

Connected through hyperedge HE-1 (induced by source paper "from news to medical cross domain discourse segmentation"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "deep contextualized word representations"
    - "scidtb discourse dependency treebank for scientific abstracts"
    - "neural discourse structure for text categorization"

Connected through hyperedge HE-2 (induced by source paper "toward fast and accurate neural discourse segmentation"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "attention is all you need"
    - "bidirectional lstm crf models for sequence tagging"
    - "adam a method for stochastic optimization"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-3 (induced by source paper "extracting aspects hierarchies using rhetorical structure theory"):
  with members:
    - "method for aspect based sentiment annotation using rhetorical analysis"
    - "a hierarchical model of reviews for aspect based sentiment analysis"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "all maximal independent sets and dynamic dominance for sparse graphs"

Connected through hyperedge HE-4 (induced by source paper "method for aspect based sentiment annotation using rhetorical analysis"):
  with members:
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"

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)