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 "zero shot entity linking with dense entity retrieval"):
  Abstract: We consider the zero-shot entity-linking challenge where each entity is defined by a short textual description, and the model must read these descriptions together with the mention context to make the final linking decisions. In this setting, retrieving entity candidates can be particularly challenging, since many of the common linking cues such as entity alias tables and link popularity are not available. In this paper, we introduce a simple and effective two stage approach for zero-shot linking, based on fine-tuned BERT architectures. In the first stage, we do retrieval in a dense space defined by a bi-encoder that independently embeds the mention context and the entity descriptions. Each candidate is then examined more carefully with a cross-encoder, that concatenates the mention and entity text. Our approach achieves a nearly 5 point absolute gain on a recently introduced zero-shot entity linking benchmark, driven largely by improvements over previous IR-based candidate retrieval. We also show that it performs well in the non-zero-shot setting, obtaining the state-of-the-art result on TACKBP-2010.

Includes members:
  Member "learning dense representations for entity retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning contextualized document representations for healthcare answer retrieval")
  Member "fine grained entity typing for domain independent entity linking":
    also connected through other hyperedges:
      (none)
  Member "zero shot entity linking by reading entity descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning contextualized document representations for healthcare answer retrieval")
  Member "poly encoders transformer architectures and pre training strategies for fast and accurate multi sentence scoring":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "xpersona evaluating multilingual personalized chatbot")
  Member "pytorch biggraph a large scale graph embedding system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank")
  Member "neural collective entity linking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unifying knowledge graph learning and recommendation towards a better understanding of user preferences")
  Member "scalable graph based individual named entity identification":
    also connected through other hyperedges:
      (none)
  Member "end to end neural entity linking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning cross context entity representations from text")

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)