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: "generating natural language inference chains"
  Abstract: The ability to reason with natural language is a fundamental prerequisite for many NLP tasks such as information extraction, machine translation and question answering. To quantify this ability, systems are commonly tested whether they can recognize textual entailment, i.e., whether one sentence can be inferred from another one. However, in most NLP applications only single source sentences instead of sentence pairs are available. Hence, we propose a new task that measures how well a model can generate an entailed sentence from a source sentence. We take entailment-pairs of the Stanford Natural Language Inference corpus and train an LSTM with attention. On a manually annotated test set we found that 82% of generated sentences are correct, an improvement of 10.3% over an LSTM baseline. A qualitative analysis shows that this model is not only capable of shortening input sentences, but also inferring new statements via paraphrasing and phrase entailment. We then apply this model recursively to input-output pairs, thereby generating natural language inference chains that can be used to automatically construct an entailment graph from source sentences. Finally, by swapping source and target sentences we can also train a model that given an input sentence invents additional information to generate a new sentence.

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 "transforming question answering datasets into natural language inference datasets"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "know what you don t know unanswerable questions for squad"
    - "learning to parse and translate improves neural machine translation"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (induced by source paper "generating contradictory neutral and entailing sentences"):
  with members:
    - "emerging language spaces learned from massively multilingual corpora"
    - "toward controlled generation of text"
    - "generating sentences by editing prototypes"
    - "natural language inference over interaction space"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "a deep generative framework for paraphrase generation"

Connected through hyperedge HE-5 (induced by source paper "multi task video captioning with video and entailment generation"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "improving lstm based video description with linguistic knowledge mined from text"
    - "jointly modeling embedding and translation to bridge video and language"
    - "unsupervised learning of video representations using lstms"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "using descriptive video services to create a large data source for video annotation research"
    - "sequence to sequence video to text"

Connected through hyperedge HE-6 (induced by source paper "detecting and explaining causes from text for a time series event"):
  with members:
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "a decomposable attention model for natural language inference"
    - "enhanced lstm for natural language inference"
    - "creating causal embeddings for question answering with minimal supervision"
    - "reasoning about entailment with neural attention"
    - "a large annotated corpus for learning natural language inference"
    - "neural machine translation by jointly learning to align and translate"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-7 (induced by source paper "constructing a natural language inference dataset using generative neural networks"):
  with members:
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "theano a python framework for fast computation of mathematical expressions"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a neural attention model for abstractive sentence summarization"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "attribute2image conditional image generation from visual attributes"
    - "building end to end dialogue systems using generative hierarchical neural network models"

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)