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: "capturing semantic similarity for entity linking with convolutional neural networks"
  Abstract: A key challenge in entity linking is making effective use of contextual information to disambiguate mentions that might refer to different entities in different contexts. We present a model that uses convolutional neural networks to capture semantic correspondence between a mention’s context and a proposed target entity. These convolutional networks operate at multiple granularities to exploit various kinds of topic information, and their rich parameterization gives them the capacity to learn which n-grams characterize different topics. We combine these networks with a sparse linear model to achieve state-of-the-art performance on multiple entity linking datasets, outperforming the prior systems of Durrett and Klein (2014) and Nguyen et al. (2014).1

Connected through hyperedge HE-1 (induced by source paper "pel bert a joint model for protocol entity linking"):
  with members:
    - "a little annotation does a lot of good a study in bootstrapping low resource named entity recognizers"
    - "investigating entity knowledge in bert with simple neural end to end entity linking"
    - "docbert bert for document classification"
    - "bridging the gap between training and inference for neural machine translation"
    - "how to fine tune bert for text classification"
    - "claim extraction in biomedical publications using deep discourse model and transfer learning"
    - "deep contextualized word representations"
    - "leveraging textual specifications for grammar based fuzzing of network protocols"

Connected through hyperedge HE-2 (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-3 (induced by source paper "what do entity centric models learn insights from entity linking in multi party dialogue"):
  with members:
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "amore upf at semeval 2018 task 4 bilstm with entity library"
    - "evaluating theory of mind in question answering"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "relnet end to end modeling of entities relations"
    - "improving coreference resolution by learning entity level distributed representations"
    - "representation of linguistic form and function in recurrent neural networks"
    - "learning global features for coreference resolution"

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"
    - "improving entity linking by modeling latent relations between mentions"
    - "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"
    - "a deep relevance matching model for ad hoc retrieval"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-5 (induced by source paper "fine grained entity typing for domain independent entity linking"):
  with members:
    - "learning to denoise distantly labeled data for entity typing"
    - "boosting entity linking performance by leveraging unlabeled documents"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-6 (induced by source paper "learning dense representations for entity retrieval"):
  with members:
    - "end to end retrieval in continuous space"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "efficient natural language response suggestion for smart reply"
    - "learning distributed representations of texts and entities from knowledge base"
    - "named entity disambiguation for noisy text"
    - "deep joint entity disambiguation with local neural attention"
    - "neural cross lingual entity linking"
    - "dynamic entity representations in neural language models"

Connected through hyperedge HE-7 (induced by source paper "learning dynamic context augmentation for global entity linking"):
  with members:
    - "neural collective entity linking based on recurrent random walk network learning"
    - "relation mention extraction from noisy data with hierarchical reinforcement learning"
    - "improving entity linking by modeling latent relations between mentions"
    - "collective entity disambiguation with structured gradient tree boosting"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "pair linking for collective entity disambiguation two could be better than all"
    - "deep joint entity disambiguation with local neural attention"
    - "deep reinforcement learning for mention ranking coreference models"

Connected through hyperedge HE-8 (induced by source paper "neural collective entity linking based on recurrent random walk network learning"):
  with members:
    - "graph based neural sentence ordering"
    - "semantic neural machine translation using amr"
    - "neural collective entity linking"
    - "end to end neural entity linking"
    - "improving entity linking by modeling latent relations between mentions"
    - "sentence state lstm for text representation"
    - "attention is all you need"
    - "deep joint entity disambiguation with local neural attention"

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)