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: "leveraging deep neural networks and knowledge graphs for entity disambiguation"
  Abstract: Entity Disambiguation aims to link mentions of ambiguous entities to a knowledge base (e.g., Wikipedia). Modeling topical coherence is crucial for this task based on the assumption that information from the same semantic context tends to belong to the same topic. This paper presents a novel deep semantic relatedness model (DSRM) based on deep neural networks (DNN) and semantic knowledge graphs (KGs) to measure entity semantic relatedness for topical coherence modeling. The DSRM is directly trained on large-scale KGs and it maps heterogeneous types of knowledge of an entity from KGs to numerical feature vectors in a latent space such that the distance between two semantically-related entities is minimized. Compared with the state-of-the-art relatedness approach proposed by (Milne and Witten, 2008a), the DSRM obtains 19.4% and 24.5% reductions in entity disambiguation errors on two publicly available datasets respectively.

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 "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 "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 "learning concept embeddings for dataless classification via efficient bag of concepts densification"):
  with members:
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "mined semantic analysis a new concept space model for semantic representation of textual data"
    - "measuring semantic relatedness using mined semantic analysis"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-5 (induced by source paper "knowledge representation learning a quantitative review"):
  with members:
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "a neural knowledge language model"
    - "analogical inference for multi relational embeddings"
    - "visual translation embedding network for visual relation detection"
    - "probabilistic reasoning via deep learning neural association models"

Connected through hyperedge HE-6 (induced by source paper "linknbed multi graph representation learning with entity linkage"):
  with members:
    - "representation learning on graphs methods and applications"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "knowledge base completion baselines strike back"
    - "rdf graph alignment with bisimulation"
    - "probabilistic reasoning via deep learning neural association models"
    - "generic statistical relational entity resolution in knowledge graphs"
    - "cross graph learning of multi relational associations"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"

Connected through hyperedge HE-7 (induced by source paper "natural language processing for ehr based computational phenotyping"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "comparing rule based and deep learning models for patient phenotyping"
    - "unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients"
    - "toward mention detection robustness with recurrent neural networks"
    - "structured prediction models for rnn based sequence labeling in clinical text"
    - "combining neural networks and log linear models to improve relation extraction"
    - "how essential are unstructured clinical narratives and information fusion to clinical trial recruitment"
    - "classifying relations via long short term memory networks along shortest dependency path"

Connected through hyperedge HE-8 (induced by source paper "deep probabilistic logic a unifying framework for indirect supervision"):
  with members:
    - "identifying civilians killed by police with distantly supervised entity event extraction"
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "learning the structure of generative models without labeled data"
    - "improving coreference resolution by learning entity level distributed representations"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "teaching machines to read and comprehend"
    - "semi supervised learning with deep generative models"
    - "neural machine translation by jointly learning to align and translate"

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)