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 "paraphrase generation from latent variable pcfgs for semantic parsing"):
  Abstract: One of the limitations of semantic parsing approaches to open-domain question answering is the lexicosyntactic gap between natural language questions and knowledge base entries -- there are many ways to ask a question, all with the same answer. In this paper we propose to bridge this gap by generating paraphrases of the input question with the goal that at least one of them will be correctly mapped to a knowledge-base query. We introduce a novel grammar model for paraphrase generation that does not require any sentence-aligned paraphrase corpus. Our key idea is to leverage the flexibility and scalability of latent-variable probabilistic context-free grammars to sample paraphrases. We do an extrinsic evaluation of our paraphrases by plugging them into a semantic parser for Freebase. Our evaluation experiments on the WebQuestions benchmark dataset show that the performance of the semantic parser significantly improves over strong baselines.

Includes members:
  Member "optimizing spectral learning for parsing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unlexicalized transition based discontinuous constituency parsing")
  Member "s mart novel tree based structured learning algorithms applied to tweet entity linking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "introduction to neural network based approaches for question answering over knowledge graphs")
  Member "question answering on freebase via relation extraction and textual evidence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "realistic re evaluation of knowledge graph completion methods an experimental study")
  Member "diversity in spectral learning for natural language parsing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimizing spectral learning for parsing")
  Member "question answering with subgraph embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation")
  Member "learning to map sentences to logical form structured classification with probabilistic categorial grammars":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "break it down a question understanding benchmark")
  Member "a joint model of language and perception for grounded attribute learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cops ref a new dataset and task on compositional referring expression comprehension")
  Member "learning to paraphrase an unsupervised approach using multiple sequence alignment":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "smart summarizer for blind people")

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)