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: "diverse and coherent paragraph generation from images"
  Abstract: Paragraph generation from images, which has gained popularity recently, is an important task for video summarization, editing, and support of the disabled. Traditional image captioning methods fall short on this front, since they aren't designed to generate long informative descriptions. Moreover, the vanilla approach of simply concatenating multiple short sentences, possibly synthesized from a classical image captioning system, doesn't embrace the intricacies of paragraphs: coherent sentences, globally consistent structure, and diversity. To address those challenges, we propose to augment paragraph generation techniques with 'coherence vectors', 'global topic vectors', and modeling of the inherent ambiguity of associating paragraphs with images, via a variational auto-encoder formulation. We demonstrate the effectiveness of the developed approach on two datasets, outperforming existing state-of-the-art techniques on both.

Connected through hyperedge HE-1 (induced by source paper "factor graph attention"):
  with members:
    - "a simple baseline for audio visual scene aware dialog"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "audio visual scene aware dialog avsd challenge at dstc7"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "structured attention networks"

Connected through hyperedge HE-2 (induced by source paper "curiosity driven reinforcement learning for diverse visual paragraph generation"):
  with members:
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "context aware visual policy network for sequence level image captioning"
    - "coarse to fine annotation enrichment for semantic segmentation learning"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "how images inspire poems generating classical chinese poetry from images with memory networks"
    - "improved image captioning via policy gradient optimization of spider"
    - "show adapt and tell adversarial training of cross domain image captioner"

Connected through hyperedge HE-3 (induced by source paper "context aware visual policy network for fine grained image captioning"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "improved image captioning via policy gradient optimization of spider"
    - "grounding referring expressions in images by variational context"
    - "actor critic sequence training for image captioning"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "learning to reason end to end module networks for visual question answering"
    - "scene graph generation by iterative message passing"
    - "interpretable convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "encoder agnostic adaptation for conditional language generation"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "pre trained language model representations for language generation"
    - "mass masked sequence to sequence pre training for language generation"
    - "pretraining based natural language generation for text summarization"
    - "language models with transformers"
    - "cross lingual language model pretraining"
    - "an embarrassingly simple approach for transfer learning from pretrained language models"
    - "unified language model pre training for natural language understanding and generation"

Connected through hyperedge HE-5 (induced by source paper "sequential latent spaces for modeling the intention during diverse image captioning"):
  with members:
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "deep contextualized word representations"
    - "fast diverse and accurate image captioning guided by part of speech"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "improved image captioning via policy gradient optimization of spider"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "convolutional image captioning"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"

Connected through hyperedge HE-6 (induced by source paper "convolutional auto encoding of sentence topics for image paragraph generation"):
  with members:
    - "pointing novel objects in image captioning"
    - "neural baby talk"
    - "exploring visual relationship for image captioning"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "recurrent topic transition gan for visual paragraph generation"
    - "image captioning with semantic attention"
    - "boosting image captioning with attributes"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"

Connected through hyperedge HE-7 (induced by source paper "generating diverse and accurate visual captions by comparative adversarial learning"):
  with members:
    - "netizen style commenting on fashion photos dataset and diversity measures"
    - "rational recurrences"
    - "discriminability objective for training descriptive captions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "improved image captioning via policy gradient optimization of spider"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "speaking the same language matching machine to human captions by adversarial training"

Connected through hyperedge HE-8 (induced by source paper "intention oriented image captions with guiding objects"):
  with members:
    - "neural baby talk"
    - "decoupled novel object captioner"
    - "improved image captioning via policy gradient optimization of spider"
    - "actor critic sequence training for image captioning"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "creativity generating diverse questions using variational autoencoders"

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.CV(Computer Vision and Pattern Recognition)