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: "probabilistic bag of hyperlinks model for entity linking"
  Abstract: Many fundamental problems in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text understanding, automatic summarization, semantic search or machine translation. Name ambiguity, word polysemy, context dependencies and a heavy-tailed distribution of entities contribute to the complexity of this problem. #R##N#We here propose a probabilistic approach that makes use of an effective graphical model to perform collective entity disambiguation. Input mentions (i.e.,~linkable token spans) are disambiguated jointly across an entire document by combining a document-level prior of entity co-occurrences with local information captured from mentions and their surrounding context. The model is based on simple sufficient statistics extracted from data, thus relying on few parameters to be learned. #R##N#Our method does not require extensive feature engineering, nor an expensive training procedure. We use loopy belief propagation to perform approximate inference. The low complexity of our model makes this step sufficiently fast for real-time usage. We demonstrate the accuracy of our approach on a wide range of benchmark datasets, showing that it matches, and in many cases outperforms, existing state-of-the-art methods.

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"
    - "improving entity linking by modeling latent relations between mentions"

Connected through hyperedge HE-2 (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"

Connected through hyperedge HE-3 (induced by source paper "joint entity linking with deep reinforcement learning"):
  with members:
    - "neural collective entity linking"
    - "improving entity linking by modeling latent relations between mentions"
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "pair linking for collective entity disambiguation two could be better than all"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "xgboost a scalable tree boosting system"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "joint learning of the embedding of words and entities for named entity disambiguation"

Connected through hyperedge HE-4 (induced by source paper "towards zero resource cross lingual entity linking"):
  with members:
    - "joint multilingual supervision for cross lingual entity linking"
    - "end to end neural entity linking"
    - "zero shot neural transfer for cross lingual entity linking"
    - "one for all towards language independent named entity linking"
    - "learning distributed representations of texts and entities from knowledge base"
    - "deep joint entity disambiguation with local neural attention"
    - "neural cross lingual entity linking"
    - "represent aggregate and constrain a novel architecture for machine reading from noisy sources"

Connected through hyperedge HE-5 (induced by source paper "end to end neural entity linking"):
  with members:
    - "improving entity linking by modeling latent relations between mentions"
    - "collective entity disambiguation with structured gradient tree boosting"
    - "end to end neural coreference resolution"
    - "deep joint entity disambiguation with local neural attention"
    - "dynamic entity representations in neural language models"
    - "empower sequence labeling with task aware neural language model"
    - "neural architectures for named entity recognition"
    - "joint learning of the embedding of words and entities for named entity disambiguation"

Connected through hyperedge HE-6 (induced by source paper "swat a system for detecting salient wikipedia entities in texts"):
  with members:
    - "wiser a semantic approach for expert finding in academia based on entity linking"
    - "allennlp a deep semantic natural language processing platform"
    - "reading wikipedia to answer open domain questions"
    - "xgboost a scalable tree boosting system"
    - "automated news suggestions for populating wikipedia entity pages"
    - "deepwalk online learning of social representations"
    - "axioms for centrality"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "improving entity linking by modeling latent relations between mentions"):
  with members:
    - "learning distributed representations of texts and entities from knowledge base"
    - "deep joint entity disambiguation with local neural attention"
    - "cotype joint extraction of typed entities and relations with knowledge bases"
    - "leveraging deep neural networks and knowledge graphs for entity disambiguation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "loopy belief propagation for approximate inference an empirical study"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "collective entity disambiguation with structured gradient tree boosting"):
  with members:
    - "deep joint entity disambiguation with local neural attention"
    - "neural cross lingual entity linking"
    - "s mart novel tree based structured learning algorithms applied to tweet entity linking"
    - "globally normalized transition based neural networks"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "leveraging deep neural networks and knowledge graphs for entity disambiguation"
    - "scikit learn machine learning in python"
    - "learning as search optimization approximate large margin methods for structured prediction"

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)