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: "pair linking for collective entity disambiguation two could be better than all"
  Abstract: Collective entity disambiguation, or collective entity linking aims to jointly resolve multiple mentions by linking them to their associated entities in a knowledge base. Previous works largely based on the underlying assumption that entities within the same document are highly related. However, the extend to which these mentioned entities are actually connected in reality is rarely studied and therefore raises interesting research questions. For the first time, this paper shows that the semantic relationships between mentioned entities within documents are in fact less dense than expected. This could be attributed to several reasons such as noise, data sparsity and knowledge base incompleteness. As a remedy, we introduces MINTREE, a new tree-based objective for the problem of entity disambiguation. The key intuition behind MINTREE is the concept of coherence relaxation which utilizes the weight of a minimum spanning tree to measure the coherence between entities. Based on this new objective, we devise a novel iterative solution for MINTREE optimization problem which we call Pair-Linking. The idea of Pair-Linking is simple: instead of considering all the given mentions, Pair-Linking iteratively selects the pair with highest confidence at each step for decision making. Via extensive experiments on 8 publicly available benchmark datasets, we show that our approach is not only more accurate but also surprisingly faster than many state-of-the-art collective linking algorithms.

Connected through hyperedge HE-1 (induced by source paper "obj glove scene based contextual object embedding"):
  with members:
    - "structure inference net object detection using scene level context and instance level relationships"
    - "semantic image synthesis via adversarial learning"
    - "relation networks for object detection"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "spatial memory for context reasoning in object detection"
    - "generative adversarial text to image synthesis"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (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"
    - "deep joint entity disambiguation with local neural attention"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "deep reinforcement learning for mention ranking coreference models"

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"
    - "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"
    - "leveraging deep neural networks and knowledge graphs for entity disambiguation"

Connected through hyperedge HE-4 (induced by source paper "fine grained named entity recognition using elmo and wikidata"):
  with members:
    - "deep contextualized word representations"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "an attentive neural architecture for fine grained entity type classification"
    - "context dependent fine grained entity type tagging"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "a survey on deep learning for named entity recognition"):
  with members:
    - "learning a unified named entity tagger from multiple partially annotated corpora for efficient adaptation"
    - "a unified mrc framework for named entity recognition"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a little annotation does a lot of good a study in bootstrapping low resource named entity recognizers"
    - "dependency guided lstm crf for named entity recognition"
    - "hierarchical contextualized representation for named entity recognition"
    - "dice loss for data imbalanced nlp tasks"
    - "attending to entities for better text understanding"

Connected through hyperedge HE-6 (induced by source paper "neural collective entity linking"):
  with members:
    - "learning distributed representations of texts and entities from knowledge base"
    - "named entity disambiguation for noisy text"
    - "deep joint entity disambiguation with local neural attention"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "probabilistic bag of hyperlinks model for entity linking"
    - "loopy belief propagation for approximate inference an empirical study"

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.IR(Information Retrieval)