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 cross lingual entity linking"):
  Abstract: A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts. The problem exacerbates with cross-lingual EL which involves linking mentions written in non-English documents to entries in the English Wikipedia: to compare textual clues across languages we need to compute similarity between textual fragments across languages. In this paper, we propose a neural EL model that trains fine-grained similarities and dissimilarities between the query and candidate document from multiple perspectives, combined with convolution and tensor networks. Further, we show that this English-trained system can be applied, in zero-shot learning, to other languages by making surprisingly effective use of multi-lingual embeddings. The proposed system has strong empirical evidence yielding state-of-the-art results in English as well as cross-lingual: Spanish and Chinese TAC 2015 datasets.

Includes members:
  Member "one for all towards language independent named entity linking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving candidate generation for low resource cross lingual entity linking")
  Member "entity linking for queries by searching wikipedia sentences":
    also connected through other hyperedges:
      (none)
  Member "capturing semantic similarity for entity linking with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pel bert a joint model for protocol entity linking")
  Member "neural networks models for entity discovery and linking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a multi task learning approach for named entity recognition using local detection")
  Member "long short term memory networks for machine reading":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "time aware large kernel convolutions")
  Member "sentence similarity learning by lexical decomposition and composition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "banditrank learning to rank using contextual bandits")
  Member "massively multilingual word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "refinement of unsupervised cross lingual word embeddings")
  Member "multi perspective context matching for machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "description based text classification with reinforcement learning")

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)