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 "constructing a natural language inference dataset using generative neural networks"):
  Abstract: Natural Language Inference is an important task for Natural Language Understanding. It is concerned with classifying the logical relation between two sentences. In this paper, we propose several text generative neural networks for constructing Natural Language Inference datasets suitable for training classifiers. To evaluate the models, we propose a new metric - the accuracy of the classifier trained on the generated dataset. The accuracy obtained with our best generative model is only 2.7% lower than the accuracy of the classifier trained on the original, manually constructed dataset. The model learns a mapping embedding for each training example. By comparing various metrics we show that datasets that obtain higher ROUGE or METEOR scores do not necessarily yield higher classification accuracies. We also provide analysis of what are the characteristics of a good dataset including the distinguishability of the generated datasets from the original one.

Includes members:
  Member "long short term memory networks for machine reading":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time aware large kernel convolutions")
  Member "a decomposable attention model for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "theano a python framework for fast computation of mathematical expressions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "generating natural language inference chains":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "visuallly grounded generation of entailments from premises")
  Member "a hierarchical latent variable encoder decoder model for generating dialogues":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention over parameters for dialogue systems")
  Member "a neural attention model for abstractive sentence summarization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "a hierarchical neural autoencoder for paragraphs and documents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "denoising based sequence to sequence pre training for text generation")
  Member "attribute2image conditional image generation from visual attributes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")

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.AI(Artificial Intelligence)