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 "nlprolog reasoning with weak unification for question answering in natural language"):
  Abstract: Rule-based models are attractive for various tasks because they inherently lead to interpretable and explainable decisions and can easily incorporate prior knowledge. However, such systems are difficult to apply to problems involving natural language, due to its linguistic variability. In contrast, neural models can cope very well with ambiguity by learning distributed representations of words and their composition from data, but lead to models that are difficult to interpret. In this paper, we describe a model combining neural networks with logic programming in a novel manner for solving multi-hop reasoning tasks over natural language. Specifically, we propose to use a Prolog prover which we extend to utilize a similarity function over pretrained sentence encoders. We fine-tune the representations for the similarity function via backpropagation. This leads to a system that can apply rule-based reasoning to natural language, and induce domain-specific rules from training data. We evaluate the proposed system on two different question answering tasks, showing that it outperforms two baselines -- BIDAF (Seo et al., 2016a) and FAST QA (Weissenborn et al., 2017b) on a subset of the WikiHop corpus and achieves competitive results on the MedHop data set (Welbl et al., 2017).

Includes members:
  Member "coarse grain fine grain coattention network for multi evidence question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "answering while summarizing multi task learning for multi hop qa with evidence extraction")
  Member "weaver deep co encoding of questions and documents for machine reading":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to retrieve reasoning paths over wikipedia graph for question answering")
  Member "dynamic integration of background knowledge in neural nlu systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "neural models for reasoning over multiple mentions using coreference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "question answering by reasoning across documents with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering")
  Member "unsupervised learning of sentence embeddings using compositional n gram features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings")
  Member "a survey on open information extraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hybrid neural tagging model for open relation extraction")
  Member "learning explanatory rules from noisy data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "analyzing differentiable fuzzy logic operators")

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)