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: "anaphora and coreference resolution a review"
  Abstract: Entity resolution aims at resolving repeated references to an entity in a document and forms a core component of natural language processing (NLP) research. This field possesses immense potential to improve the performance of other NLP fields like machine translation, sentiment analysis, paraphrase detection, summarization, etc. The area of entity resolution in NLP has seen proliferation of research in two separate sub-areas namely: anaphora resolution and coreference resolution. Through this review article, we aim at clarifying the scope of these two tasks in entity resolution. We also carry out a detailed analysis of the datasets, evaluation metrics and research methods that have been adopted to tackle this NLP problem. This survey is motivated with the aim of providing the reader with a clear understanding of what constitutes this NLP problem and the issues that require attention.

Connected through hyperedge HE-1 (induced by source paper "extraction and analysis of fictional character networks a survey"):
  with members:
    - "remembering winter was coming character oriented video summaries of tv series"
    - "conversational networks for automatic online moderation"
    - "remembering winter was coming"
    - "predicting kills in game of thrones using network properties"
    - "multilayer network model of movie script"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "i will be there for you six friends in a clique"
    - "the one comparing narrative social network extraction techniques"

Connected through hyperedge HE-2 (induced by source paper "a neural entity coreference resolution review"):
  with members:
    - "context in neural machine translation a review of models and evaluations"
    - "incorporating context and external knowledge for pronoun coreference resolution"
    - "gendered ambiguous pronouns shared task boosting model confidence by evidence pooling"
    - "knowledge aware pronoun coreference resolution"
    - "measuring bias in contextualized word representations"
    - "improving generalization in coreference resolution via adversarial training"
    - "look again at the syntax relational graph convolutional network for gendered ambiguous pronoun resolution"
    - "gender bias in contextualized word embeddings"

Connected through hyperedge HE-3 (induced by source paper "where s my head definition dataset and models for numeric fused heads identification and resolution"):
  with members:
    - "neural coreference resolution with deep biaffine attention by joint mention detection and mention clustering"
    - "numeracy for language models evaluating and improving their ability to predict numbers"
    - "allennlp a deep semantic natural language processing platform"
    - "neural arithmetic logic units"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "look again at the syntax relational graph convolutional network for gendered ambiguous pronoun resolution"):
  with members:
    - "neural coreference resolution with deep biaffine attention by joint mention detection and mention clustering"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural coreference resolution"
    - "attention is all you need"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "gated graph sequence neural networks"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "msnet a bert based network for gendered pronoun resolution"):
  with members:
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "gender bias in coreference resolution"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-6 (induced by source paper "resource mention extraction for mooc discussion forums"):
  with members:
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "end to end neural coreference resolution"
    - "learning global features for coreference resolution"
    - "deep reinforcement learning for mention ranking coreference models"
    - "neural architectures for named entity recognition"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "bidirectional lstm crf models for sequence tagging"
    - "character aware neural 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)