human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "neural collective entity linking"):
  Abstract: Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local contexts to resolve entities independently, which may usually fail due to the data sparsity of local information. To address this issue, we propose a novel neural model for collective entity linking, named as NCEL. NCEL applies Graph Convolutional Network to integrate both local contextual features and global coherence information for entity linking. To improve the computation efficiency, we approximately perform graph convolution on a subgraph of adjacent entity mentions instead of those in the entire text. We further introduce an attention scheme to improve the robustness of NCEL to data noise and train the model on Wikipedia hyperlinks to avoid overfitting and domain bias. In experiments, we evaluate NCEL on five publicly available datasets to verify the linking performance as well as generalization ability. We also conduct an extensive analysis of time complexity, the impact of key modules, and qualitative results, which demonstrate the effectiveness and efficiency of our proposed method.

Includes members:
  Member "pair linking for collective entity disambiguation two could be better than all":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "obj glove scene based contextual object embedding")
  Member "learning distributed representations of texts and entities from knowledge base":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning cross context entity representations from text")
  Member "named entity disambiguation for noisy text":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "wikipedia2vec an efficient toolkit for learning and visualizing the embeddings of words and entities from wikipedia")
  Member "deep joint entity disambiguation with local neural attention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving entity linking by modeling latent entity type information")
  Member "joint learning of the embedding of words and entities for named entity disambiguation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning cross context entity representations from text")
  Member "probabilistic bag of hyperlinks model for entity linking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving entity linking by modeling latent entity type information")
  Member "loopy belief propagation for approximate inference an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "analyzing differentiable fuzzy logic operators")

Question: Please tell me which class the source paper 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)