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 "vision and language from visual perception to content creation"):
  Abstract: Vision and language are two fundamental capabilities of human intelligence. Humans routinely perform tasks through the interactions between vision and language, supporting the uniquely human capacity to talk about what they see or hallucinate a picture on a natural-language description. The valid question of how language interacts with vision motivates us researchers to expand the horizons of computer vision area. In particular, "vision to language" is probably one of the most popular topics in the past five years, with a significant growth in both volume of publications and extensive applications, e.g., captioning, visual question answering, visual dialog, language navigation, etc. Such tasks boost visual perception with more comprehensive understanding and diverse linguistic representations. Going beyond the progresses made in "vision to language," language can also contribute to vision understanding and offer new possibilities of visual content creation, i.e., "language to vision." The process performs as a prism through which to create visual content conditioning on the language inputs. This paper reviews the recent advances along these two dimensions: "vision to language" and "language to vision." More concretely, the former mainly focuses on the development of image/video captioning, as well as typical encoder-decoder structures and benchmarks, while the latter summarizes the technologies of visual content creation. The real-world deployment or services of vision and language are elaborated as well.

Includes members:
  Member "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatio temporal ranked attention networks for video captioning")
  Member "hierarchy parsing for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deconfounded image captioning a causal retrospect")
  Member "unsupervised person image generation with semantic parsing transformation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to transfer texture from clothing images to 3d humans")
  Member "temporal deformable convolutional encoder decoder networks for video captioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "non autoregressive video captioning with iterative refinement")
  Member "generating multiple objects at spatially distinct locations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "joint discriminative and generative learning for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "relationship aware spatial perception fusion for realistic scene layout generation":
    also connected through other hyperedges:
      (none)
  Member "photo realistic facial details synthesis from single image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards high fidelity 3d face reconstruction from in the wild images using graph convolutional networks")

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)