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 relation prediction for simple question answering over knowledge graph"):
  Abstract: Relation extraction from simple questions aims to capture the relation of a factoid question with one underlying relation from a set of predefined ones ina knowledge base. Most recent methods take advantage of neural networks for matching a question with all relations in order to find the best relation that is expressed by that question. In this paper, we propose an instance-based method to find similar questions of a new question, in the sense of their relations, to predict its mentioned relation. The motivation roots in the fact that a relation can be expressed with different forms of question and these forms mostly share similar terms or concepts. Our experiments on the SimpleQuestions dataset show that the proposed model achieved better accuracy compared to the state-of-the-art relation extraction models.

Includes members:
  Member "anmm ranking short answer texts with attention based neural matching model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")
  Member "improved neural relation detection for knowledge base question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pretrained transformers for simple question answering over knowledge graphs")
  Member "end to end neural ad hoc ranking with kernel pooling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "strong baselines for simple question answering over knowledge graphs with and without neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pretrained transformers for simple question answering over knowledge graphs")
  Member "bilateral multi perspective matching for natural language sentences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")
  Member "attentive pooling networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cut based graph learning networks to discover compositional structure of sequential video data")
  Member "a deep relevance matching model for ad hoc retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")
  Member "learning to match using local and distributed representations of text for web search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")

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)