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 entity linking by modeling latent relations between mentions"
  Abstract: Entity linking involves aligning textual mentions of named entities to their corresponding entries in a knowledge base. Entity linking systems often exploit relations between textual mentions in a document (e.g., coreference) to decide if the linking decisions are compatible. Unlike previous approaches, which relied on supervised systems or heuristics to predict these relations, we treat relations as latent variables in our neural entity-linking model. We induce the relations without any supervision while optimizing the entity-linking system in an end-to-end fashion. Our multi-relational model achieves the best reported scores on the standard benchmark (AIDA-CoNLL) and substantially outperforms its relation-agnostic version. Its training also converges much faster, suggesting that the injected structural bias helps to explain regularities in the training data.

Connected through hyperedge HE-1 (induced by source paper "improving entity linking by modeling latent entity type information"):
  with members:
    - "joint learning of named entity recognition and entity linking"
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "zero shot open entity typing as type compatible grounding"
    - "boosting entity linking performance by leveraging unlabeled documents"
    - "learning dynamic context augmentation for global entity linking"
    - "distant learning for entity linking with automatic noise detection"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "ultra fine entity typing"

Connected through hyperedge HE-2 (induced by source paper "novel entity discovery from web tables"):
  with members:
    - "auto completion for data cells in relational tables"
    - "matchzoo a learning practicing and developing system for neural text matching"
    - "end to end neural entity linking"
    - "ad hoc table retrieval using semantic similarity"
    - "on the fly table generation"
    - "deep joint entity disambiguation with local neural attention"
    - "the birth of collective memories analyzing emerging entities in text streams"

Connected through hyperedge HE-3 (induced by source paper "enteval a holistic evaluation benchmark for entity representations"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "joint learning of named entity recognition and entity linking"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "evaluation benchmarks and learning criteriafor discourse aware sentence representations"
    - "ernie enhanced representation through knowledge integration"
    - "zero shot entity linking by reading entity descriptions"
    - "socialiqa commonsense reasoning about social interactions"
    - "seeing things from a different angle discovering diverse perspectives about claims"

Connected through hyperedge HE-4 (induced by source paper "latte latent type modeling for biomedical entity linking"):
  with members:
    - "medmentions a large biomedical corpus annotated with umls concepts"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "a deep relevance matching model for ad hoc retrieval"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-5 (induced by source paper "joint learning of named entity recognition and entity linking"):
  with members:
    - "neural collective entity linking"
    - "end to end neural entity linking"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "collective entity disambiguation with structured gradient tree boosting"
    - "learning distributed representations of texts and entities from knowledge base"
    - "deep joint entity disambiguation with local neural attention"

Connected through hyperedge HE-6 (induced by source paper "graph neural networks with generated parameters for relation extraction"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "cross sentence n ary relation extraction with graph lstms"
    - "a simple neural network module for relational reasoning"
    - "scene graph generation by iterative message passing"
    - "neural message passing for quantum chemistry"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

Connected through hyperedge HE-7 (induced by source paper "simultaneously linking entities and extracting relations from biomedical text without mention level supervision"):
  with members:
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "deep joint entity disambiguation with local neural attention"
    - "generalizing to unseen entities and entity pairs with row less universal schema"

Connected through hyperedge HE-8 (induced by source paper "zero shot entity linking by reading entity descriptions"):
  with members:
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "multi source domain adaptation with mixture of experts"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "knowledge based word sense disambiguation using topic models"
    - "universal language model fine tuning for text classification"
    - "hierarchical losses and new resources for fine grained entity typing and linking"

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)