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: "recurrent topic transition gan for visual paragraph generation"
  Abstract: A natural image usually conveys rich semantic content and can be viewed from different angles. Existing image description methods are largely restricted by small sets of biased visual paragraph annotations, and fail to cover rich underlying semantics. In this paper, we investigate a semi-supervised paragraph generative framework that is able to synthesize diverse and semantically coherent paragraph descriptions by reasoning over local semantic regions and exploiting linguistic knowledge. The proposed Recurrent Topic-Transition Generative Adversarial Network (RTT-GAN) builds an adversarial framework between a structured paragraph generator and multi-level paragraph discriminators. The paragraph generator generates sentences recurrently by incorporating region-based visual and language attention mechanisms at each step. The quality of generated paragraph sentences is assessed by multi-level adversarial discriminators from two aspects, namely, plausibility at sentence level and topic-transition coherence at paragraph level. The joint adversarial training of RTT-GAN drives the model to generate realistic paragraphs with smooth logical transition between sentence topics. Extensive quantitative experiments on image and video paragraph datasets demonstrate the effectiveness of our RTT-GAN in both supervised and semi-supervised settings. Qualitative results on telling diverse stories for an image also verify the interpretability of RTT-GAN.

Connected through hyperedge HE-1 (induced by source paper "robust explanations for visual question answering"):
  with members:
    - "explanation vs attention a two player game to obtain attention for vqa"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "multimodal differential network for visual question generation"
    - "differential attention for visual question answering"
    - "toward controlled generation of text"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-2 (induced by source paper "video storytelling textual summaries for events"):
  with members:
    - "ffnet video fast forwarding via reinforcement learning"
    - "predicting visual features from text for image and video caption retrieval"
    - "unsupervised learning of view invariant action representations"
    - "query adaptive video summarization via quality aware relevance estimation"
    - "improved image captioning via policy gradient optimization of spider"
    - "dual glance model for deciphering social relationships"
    - "summarization of user generated sports video by using deep action recognition features"
    - "attention transfer from web images for video recognition"

Connected through hyperedge HE-3 (induced by source paper "recent advances of image steganography with generative adversarial networks"):
  with members:
    - "steganogan high capacity image steganography with gans"
    - "cnn based adversarial embedding for image steganography"
    - "stegastamp invisible hyperlinks in physical photographs"
    - "deep learning in steganography and steganalysis from 2015 to 2018"
    - "spatial image steganography based on generative adversarial network"
    - "adaptive spatial steganography based on probability controlled adversarial examples"
    - "generative adversarial networks an overview"
    - "an introduction to image synthesis with generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "curiosity driven reinforcement learning for diverse visual paragraph generation"):
  with members:
    - "exploration by random network distillation"
    - "diverse and coherent paragraph generation from images"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "context aware visual policy network for fine grained image captioning"):
  with members:
    - "diverse and coherent paragraph generation from images"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "revisiting paraphrase question generator using pairwise discriminator"):
  with members:
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "toward controlled generation of text"
    - "learning to ask neural question generation for reading comprehension"
    - "adversarial learning for neural dialogue generation"
    - "long text generation via adversarial training with leaked information"
    - "a deep generative framework for paraphrase generation"
    - "paraphrase generation with deep reinforcement learning"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-7 (induced by source paper "knowledge driven encode retrieve paraphrase for medical image report generation"):
  with members:
    - "image transformer"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "neural baby talk"
    - "on the automatic generation of medical imaging reports"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "relational inductive biases deep learning and graph networks"
    - "linguistically informed self attention for semantic role labeling"
    - "toward controlled generation of text"

Connected through hyperedge HE-8 (induced by source paper "captioning near future activity sequences"):
  with members:
    - "move forward and tell a progressive generator of video descriptions"
    - "when will you do what anticipating temporal occurrences of activities"
    - "dense captioning events in videos"
    - "estimating human intent for physical human robot co manipulation"
    - "towards automatic learning of procedures from web instructional videos"
    - "spice semantic propositional image caption evaluation"
    - "first person activity forecasting with online inverse reinforcement learning"
    - "recognizing fine grained and composite activities using hand centric features and script data"

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)