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: "end to end neural entity linking"
  Abstract: Entity Linking (EL) is an essential task for semantic text understanding and information extraction. Popular methods separately address the Mention Detection (MD) and Entity Disambiguation (ED) stages of EL, without leveraging their mutual dependency. We here propose the first neural end-to-end EL system that jointly discovers and links entities in a text document. The main idea is to consider all possible spans as potential mentions and learn contextual similarity scores over their entity candidates that are useful for both MD and ED decisions. Key components are context-aware mention embeddings, entity embeddings and a probabilistic mention - entity map, without demanding other engineered features. Empirically, we show that our end-to-end method significantly outperforms popular systems on the Gerbil platform when enough training data is available. Conversely, if testing datasets follow different annotation conventions compared to the training set (e.g. queries/ tweets vs news documents), our ED model coupled with a traditional NER system offers the best or second best EL accuracy.

Connected through hyperedge HE-1 (induced by source paper "learning cross context entity representations from text"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "additive margin softmax for face verification"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "hierarchical losses and new resources for fine grained entity typing and linking"

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"
    - "ad hoc table retrieval using semantic similarity"
    - "improving entity linking by modeling latent relations between mentions"
    - "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 "knowledge enhanced contextual word representations"):
  with members:
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"
    - "matching the blanks distributional similarity for relation learning"
    - "ernie enhanced language representation with informative entities"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "tucker tensor factorization for knowledge graph completion"
    - "simple bert models for relation extraction and semantic role labeling"

Connected through hyperedge HE-4 (induced by source paper "towards complex text to sql in cross domain database with intermediate representation"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "semantic parsing with syntax and table aware sql generation"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"

Connected through hyperedge HE-5 (induced by source paper "multifc a real world multi domain dataset for evidence based fact checking of claims"):
  with members:
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "twowingos a two wing optimization strategy for evidential claim verification"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"

Connected through hyperedge HE-6 (induced by source paper "neural attentive bag of entities model for text classification"):
  with members:
    - "neural collective entity linking"
    - "studio ousia s quiz bowl question answering system"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "learning distributed representations of texts and entities from knowledge base"
    - "named entity disambiguation for noisy text"
    - "deep joint entity disambiguation with local neural attention"

Connected through hyperedge HE-7 (induced by source paper "conna addressing name disambiguation on the fly"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "name disambiguation in anonymized graphs using network embedding"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "deep reinforcement learning for mention ranking coreference models"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "text matching as image recognition"
    - "match srnn modeling the recursive matching structure with spatial rnn"

Connected through hyperedge HE-8 (induced by source paper "investigating entity knowledge in bert with simple neural end to end entity linking"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "bert rediscovers the classical nlp pipeline"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "ernie enhanced language representation with informative entities"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)