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: "neural net models for open domain discourse coherence"
  Abstract: Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. Yet existing models of coherence focus on measuring individual aspects of coherence (lexical overlap, rhetorical structure, entity centering) in narrow domains. #R##N#In this paper, we describe domain-independent neural models of discourse coherence that are capable of measuring multiple aspects of coherence in existing sentences and can maintain coherence while generating new sentences. We study both discriminative models that learn to distinguish coherent from incoherent discourse, and generative models that produce coherent text, including a novel neural latent-variable Markovian generative model that captures the latent discourse dependencies between sentences in a text. #R##N#Our work achieves state-of-the-art performance on multiple coherence evaluations, and marks an initial step in generating coherent texts given discourse contexts.

Connected through hyperedge HE-1 (induced by source paper "approximating interactive human evaluation with self play for open domain dialog systems"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "unifying human and statistical evaluation for natural language generation"
    - "hierarchical reinforcement learning for open domain dialog"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "a hierarchical latent structure for variational conversation modeling"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"

Connected through hyperedge HE-2 (induced by source paper "content word based sentence decoding and evaluating for open domain neural response generation"):
  with members:
    - "universal dependency parsing from scratch"
    - "coarse to fine decoding for neural semantic parsing"
    - "toward controlled generation of text"
    - "latent intention dialogue models"
    - "event representations for automated story generation with deep neural nets"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a persona based neural conversation model"

Connected through hyperedge HE-3 (induced by source paper "transfertransfo a transfer learning approach for neural network based conversational agents"):
  with members:
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "deep contextualized word representations"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "adversarial learning for neural dialogue generation"
    - "a deep reinforcement learning chatbot"
    - "a simple fast diverse decoding algorithm for neural generation"

Connected through hyperedge HE-4 (induced by source paper "deep attentive ranking networks for learning to order sentences"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "context is key new approaches to neural coherence modeling"
    - "an efficient framework for learning sentence representations"
    - "attention is all you need"
    - "sentence ordering and coherence modeling using recurrent neural networks"
    - "sort story sorting jumbled images and captions into stories"
    - "neural sentence ordering"

Connected through hyperedge HE-5 (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"
    - "coherence modeling of asynchronous conversations a neural entity grid approach"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "a cross domain transferable neural coherence model"):
  with members:
    - "coherence modeling of asynchronous conversations a neural entity grid approach"
    - "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"
    - "skip thought vectors"

Connected through hyperedge HE-7 (induced by source paper "a unified neural coherence model"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "deep contextualized word representations"
    - "xception deep learning with depthwise separable convolutions"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"
    - "catching the drift probabilistic content models with applications to generation and summarization"

Connected through hyperedge HE-8 (induced by source paper "multi task learning for universal sentence representations what syntactic and semantic information is captured"):
  with members:
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "end to end neural coreference resolution"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "semi supervised multitask learning for sequence labeling"
    - "a generalized recurrent neural architecture for text classification with multi task learning"

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)