human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "paraphrase generation with latent bag of words"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "topic guided variational autoencoders for text generation"
    - "step by step separating planning from realization in neural data to text generation"
    - "a tutorial on deep latent variable models of natural language"
    - "end to end content and plan selection for data to text generation"
    - "learning neural templates for text generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"

Connected through hyperedge HE-2 (induced by source paper "paraphrase generation with deep reinforcement learning"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "word embedding attention network generating words by querying distributed word representations for paraphrase generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "a continuously growing dataset of sentential paraphrases"
    - "learning to paraphrase for question answering"
    - "attention is all you need"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "learning paraphrastic sentence embeddings from back translated bitext"

Connected through hyperedge HE-3 (induced by source paper "cross domain semantic parsing via paraphrasing"):
  with members:
    - "universal semantic parsing"
    - "neural semantic parsing over multiple knowledge bases"
    - "language to logical form with neural attention"
    - "data recombination for neural semantic parsing"
    - "question answering on freebase via relation extraction and textual evidence"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-4 (induced by source paper "learning to paraphrase for question answering"):
  with members:
    - "universal semantic parsing"
    - "improving neural machine translation models with monolingual data"
    - "neural variational inference for text processing"
    - "neural machine translation of rare words with subword units"
    - "learning natural language inference with lstm"
    - "deep learning for answer sentence selection"
    - "distributed representations of sentences and documents"
    - "open question answering with weakly supervised embedding models"

Connected through hyperedge HE-5 (induced by source paper "split and rephrase"):
  with members:
    - "multi source neural translation"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"
    - "neural responding machine for short text conversation"
    - "neural machine translation by jointly learning to align and translate"

Question: Please tell me which class the center node 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)