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 "end to end entity linking and disambiguation leveraging word and knowledge graph embeddings"):
  Abstract: Entity linking - connecting entity mentions in a natural language utterance to knowledge graph (KG) entities is a crucial step for question answering over KGs. It is often based on measuring the string similarity between the entity label and its mention in the question. The relation referred to in the question can help to disambiguate between entities with the same label. This can be misleading if an incorrect relation has been identified in the relation linking step. However, an incorrect relation may still be semantically similar to the relation in which the correct entity forms a triple within the KG; which could be captured by the similarity of their KG embeddings. Based on this idea, we propose the first end-to-end neural network approach that employs KG as well as word embeddings to perform joint relation and entity classification of simple questions while implicitly performing entity disambiguation with the help of a novel gating mechanism. An empirical evaluation shows that the proposed approach achieves a performance comparable to state-of-the-art entity linking while requiring less post-processing.

Includes members:
  Member "simplequestions nearly solved a new upperbound and baseline approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pretrained transformers for simple question answering over knowledge graphs")
  Member "improved neural relation detection for knowledge base question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural relation prediction for simple question answering over knowledge graph")
  Member "strong baselines for simple question answering over knowledge graphs with and without neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural relation prediction for simple question answering over knowledge graph")
  Member "convolutional 2d knowledge graph embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "knowledge graphs")
  Member "cfo conditional focused neural question answering with large scale knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural relation prediction for simple question answering over knowledge graph")
  Member "simple question answering by attentive convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural relation prediction for simple question answering over knowledge graph")
  Member "character level question answering with attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")
  Member "question answering on freebase via relation extraction and textual evidence":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "realistic re evaluation of knowledge graph completion methods an experimental study")

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)