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 "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.

Includes members:
  Member "toward controlled generation of text":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust explanations for visual question answering")
  Member "on unifying deep generative models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving variational autoencoder with deep feature consistent and generative adversarial training")
  Member "deep variation structured reinforcement learning for visual relationship and attribute detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "seqgan sequence generative adversarial nets with policy gradient":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "review networks for caption generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vision and language from visual perception to content creation")
  Member "image captioning with semantic attention":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "incorporating copying mechanism in sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "is faster r cnn doing well for pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "psc net learning part spatial co occurence for occluded pedestrian detection")

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.CV(Computer Vision and Pattern Recognition)