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 "learning dynamic context augmentation for global entity linking"):
  Abstract: Despite of the recent success of collective entity linking (EL) methods, these "global" inference methods may yield sub-optimal results when the "all-mention coherence" assumption breaks, and often suffer from high computational cost at the inference stage, due to the complex search space. In this paper, we propose a simple yet effective solution, called Dynamic Context Augmentation (DCA), for collective EL, which requires only one pass through the mentions in a document. DCA sequentially accumulates context information to make efficient, collective inference, and can cope with different local EL models as a plug-and-enhance module. We explore both supervised and reinforcement learning strategies for learning the DCA model. Extensive experiments show the effectiveness of our model with different learning settings, base models, decision orders and attention mechanisms.

Includes members:
  Member "neural collective entity linking based on recurrent random walk network learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "porous lattice based transformer encoder for chinese ner")
  Member "relation mention extraction from noisy data with hierarchical reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "improving entity linking by modeling latent relations between mentions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving entity linking by modeling latent entity type information")
  Member "collective entity disambiguation with structured gradient tree boosting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint learning of named entity recognition and entity linking")
  Member "deeptype multilingual entity linking by neural type system evolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning cross context entity representations from text")
  Member "pair linking for collective entity disambiguation two could be better than all":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "obj glove scene based contextual object embedding")
  Member "deep joint entity disambiguation with local neural attention":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving entity linking by modeling latent entity type information")
  Member "capturing semantic similarity for entity linking with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pel bert a joint model for protocol entity linking")

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)