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: "coherence modeling of asynchronous conversations a neural entity grid approach"
  Abstract: We propose a novel coherence model for written asynchronous conversations (e.g., forums, emails), and show its applications in coherence assessment and thread reconstruction tasks. We conduct our research in two steps. First, we propose improvements to the recently proposed neural entity grid model by lexicalizing its entity transitions. Then, we extend the model to asynchronous conversations by incorporating the underlying conversational structure in the entity grid representation and feature computation. Our model achieves state of the art results on standard coherence assessment tasks in monologue and conversations outperforming existing models. We also demonstrate its effectiveness in reconstructing thread structures.

Connected through hyperedge HE-1 (induced by source paper "multi task learning for coherence modeling"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "discourse coherence in the wild a dataset evaluation and methods"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "auxiliary objectives for neural error detection models"
    - "sentence ordering and coherence modeling using recurrent neural networks"
    - "neural net models for open domain discourse coherence"

Connected through hyperedge HE-2 (induced by source paper "a cross domain transferable neural coherence model"):
  with members:
    - "learning to write with cooperative discriminators"
    - "deep contextualized word representations"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "axiomatic attribution for deep networks"
    - "sentence ordering and coherence modeling using recurrent neural networks"
    - "neural net models for open domain discourse coherence"
    - "skip thought vectors"

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)