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: "scidtb discourse dependency treebank for scientific abstracts"
  Abstract: Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question answering. In this paper, we present SciDTB, a domain-specific discourse treebank annotated on scientific articles. Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is flexible and simplified to some extent but do not sacrifice structural integrity. We discuss the labeling framework, annotation workflow and some statistics about SciDTB. Furthermore, our treebank is made as a benchmark for evaluating discourse dependency parsers, on which we provide several baselines as fundamental work.

Connected through hyperedge HE-1 (induced by source paper "zero shot chinese discourse dependency parsing via cross lingual mapping"):
  with members:
    - "character level chinese english translation through ascii encoding"
    - "upping the ante towards a better benchmark for chinese to english machine translation"
    - "chinese discourse segmentation using bilingual discourse commonality"
    - "structured attention networks"
    - "learning structured text representations"

Connected through hyperedge HE-2 (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"
    - "neural discourse structure for text categorization"
    - "two pass discourse segmentation with pairing and global features"

Connected through hyperedge HE-3 (induced by source paper "a deep sequential model for discourse parsing on multi party dialogues"):
  with members:
    - "dialogue act recognition via crf attentive structured network"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "cross lingual rst discourse parsing"
    - "better document level sentiment analysis from rst discourse parsing"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)