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: "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.

Connected through hyperedge HE-1 (induced by source paper "visuallly grounded generation of entailments from premises"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "visual entailment a novel task for fine grained image understanding"
    - "quantifying the amount of visual information used by neural caption generators"
    - "annotation artifacts in natural language inference data"
    - "object counts bringing explicit detections back into image captioning"
    - "grounded textual entailment"
    - "where to put the image in an image caption generator"
    - "foil it find one mismatch between image and language caption"

Connected through hyperedge HE-2 (induced by source paper "grounded textual entailment"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "evaluating multimodal representations on sentence similarity vsts visual semantic textual similarity dataset"
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"
    - "examining cooperation in visual dialog models"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "bilateral multi perspective matching for natural language sentences"

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.AI(Artificial Intelligence)