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: "improving implicit discourse relation classification by modeling inter dependencies of discourse units in a paragraph"
  Abstract: We argue that semantic meanings of a sentence or clause can not be interpreted independently from the rest of a paragraph, or independently from all discourse relations and the overall paragraph-level discourse structure. With the goal of improving implicit discourse relation classification, we introduce a paragraph-level neural networks that model inter-dependencies between discourse units as well as discourse relation continuity and patterns, and predict a sequence of discourse relations in a paragraph. Experimental results show that our model outperforms the previous state-of-the-art systems on the benchmark corpus of PDTB.

Connected through hyperedge HE-1 (induced by source paper "unsupervised adversarial domain adaptation for implicit discourse relation classification"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "spectral normalization for generative adversarial networks"
    - "cross lingual distillation for text classification"
    - "aspect augmented adversarial networks for domain adaptation"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "adversarial discriminative domain adaptation"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "from source to target and back symmetric bi directional adaptive gan"

Connected through hyperedge HE-2 (induced by source paper "memorizing all for implicit discourse relation recognition"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "a recurrent neural model with attention for the recognition of chinese implicit discourse relations"
    - "implicit discourse relation classification via multi task neural networks"
    - "deep biaffine attention for neural dependency parsing"
    - "language modeling with gated convolutional networks"
    - "key value memory networks for directly reading documents"
    - "recognizing implicit discourse relations via repeated reading neural networks with multi level attention"

Connected through hyperedge HE-3 (induced by source paper "semantic graph convolutional network for implicit discourse relation classification"):
  with members:
    - "modeling coherence for discourse neural machine translation"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph convolutional networks for text classification"
    - "role semantics for better models of implicit discourse relations"
    - "a recurrent neural model with attention for the recognition of chinese implicit discourse relations"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "implicit discourse relation identification for open domain dialogues"):
  with members:
    - "advancing the state of the art in open domain dialog systems through the alexa prize"
    - "deep enhanced representation for implicit discourse relation recognition"
    - "talking to myself self dialogues as data for conversational agents"
    - "deep contextualized word representations"
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "slugnerds a named entity recognition tool for open domain dialogue systems"
    - "conversational ai the science behind the alexa prize"
    - "combining search with structured data to create a more engaging user experience in open domain dialogue"

Connected through hyperedge HE-5 (induced by source paper "zero shot transfer for implicit discourse relation classification"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "dissent sentence representation learning from explicit discourse relations"

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)