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: "collective entity disambiguation with structured gradient tree boosting"
  Abstract: We present a gradient-tree-boosting-based structured learning model for jointly disambiguating named entities in a document. Gradient tree boosting is a widely used machine learning algorithm that underlies many top-performing natural language processing systems. Surprisingly, most works limit the use of gradient tree boosting as a tool for regular classification or regression problems, despite the structured nature of language. To the best of our knowledge, our work is the first one that employs the structured gradient tree boosting (SGTB) algorithm for collective entity disambiguation. By defining global features over previous disambiguation decisions and jointly modeling them with local features, our system is able to produce globally optimized entity assignments for mentions in a document. Exact inference is prohibitively expensive for our globally normalized model. To solve this problem, we propose Bidirectional Beam Search with Gold path (BiBSG), an approximate inference algorithm that is a variant of the standard beam search algorithm. BiBSG makes use of global information from both past and future to perform better local search. Experiments on standard benchmark datasets show that SGTB significantly improves upon published results. Specifically, SGTB outperforms the previous state-of-the-art neural system by near 1\% absolute accuracy on the popular AIDA-CoNLL dataset.

Connected through hyperedge HE-1 (induced by source paper "joint learning of named entity recognition and entity linking"):
  with members:
    - "neural collective entity linking"
    - "end to end neural entity linking"
    - "improving entity linking by modeling latent relations between mentions"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "learning distributed representations of texts and entities from knowledge base"
    - "deep joint entity disambiguation with local neural attention"

Connected through hyperedge HE-2 (induced by source paper "pre training of deep contextualized embeddings of words and entities for named entity disambiguation"):
  with members:
    - "neural collective entity linking"
    - "improving entity linking by modeling latent relations between mentions"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"
    - "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-3 (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"
    - "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"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "deep reinforcement learning for mention ranking coreference models"

Connected through hyperedge HE-4 (induced by source paper "end to end neural entity linking"):
  with members:
    - "improving entity linking by modeling latent relations between mentions"
    - "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"
    - "analysis of named entity recognition and linking for tweets"

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)