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 automated essay scoring and coherence modeling for adversarially crafted input"
  Abstract: We demonstrate that current state-of-the-art approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent sequences of sentences. We develop a neural model of local coherence that can effectively learn connectedness features between sentences, and propose a framework for integrating and jointly training the local coherence model with a state-of-the-art AES model. We evaluate our approach against a number of baselines and experimentally demonstrate its effectiveness on both the AES task and the task of flagging adversarial input, further contributing to the development of an approach that strengthens the validity of neural essay scoring models.

Connected through hyperedge HE-1 (induced by source paper "rc qed evaluating natural language derivations in multi hop reading comprehension"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "automated fact checking task formulations methods and future directions"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "neural models for reasoning over multiple mentions using coreference"
    - "multi hop inference for sentence level textgraphs how challenging is meaningfully combining information for science question answering"
    - "e snli natural language inference with natural language explanations"
    - "question answering by reasoning across documents with graph convolutional networks"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "predicting ted talk ratings from language and prosody"):
  with members:
    - "fostering user engagement rhetorical devices for applause generation learnt from ted talks"
    - "convolutional neural network for humor recognition"
    - "regularizing and optimizing lstm language models"
    - "using argument based features to predict and analyse review helpfulness"
    - "globally normalized transition based neural networks"
    - "automatic text scoring using neural networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "a causality guided prediction of the ted talk ratings from the speech transcripts using neural networks"):
  with members:
    - "fostering user engagement rhetorical devices for applause generation learnt from ted talks"
    - "convolutional neural network for humor recognition"
    - "regularizing and optimizing lstm language models"
    - "using argument based features to predict and analyse review helpfulness"
    - "globally normalized transition based neural networks"
    - "automatic text scoring using neural networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "automated essay scoring based on two stage learning"):
  with members:
    - "automated essay scoring with string kernels and word embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "xgboost a scalable tree boosting system"
    - "automatic text scoring using neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "learning sentence embeddings for coherence modelling and beyond"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "text coherence analysis based on deep neural network"
    - "a deep reinforced model for abstractive summarization"
    - "sentence ordering and coherence modeling using recurrent neural networks"
    - "neural sentence ordering"
    - "bag of tricks for efficient text classification"
    - "end to end neural sentence ordering using pointer network"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"

Connected through hyperedge HE-7 (induced by source paper "coherence models for dialogue"):
  with members:
    - "deep reinforcement learning for dialogue generation"
    - "a latent variable recurrent neural network for discourse relation language models"

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)