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: "detailed garment recovery from a single view image"
  Abstract: Most recent garment capturing techniques rely on acquiring multiple views of clothing, which may not always be readily available, especially in the case of pre-existing photographs from the web. As an alternative, we pro- pose a method that is able to compute a rich and realistic 3D model of a human body and its outfits from a single photograph with little human in- teraction. Our algorithm is not only able to capture the global shape and geometry of the clothing, it can also extract small but important details of cloth, such as occluded wrinkles and folds. Unlike previous methods using full 3D information (i.e. depth, multi-view images, or sampled 3D geom- etry), our approach achieves detailed garment recovery from a single-view image by using statistical, geometric, and physical priors and a combina- tion of parameter estimation, semantic parsing, shape recovery, and physics- based cloth simulation. We demonstrate the effectiveness of our algorithm by re-purposing the reconstructed garments for virtual try-on and garment transfer applications, as well as cloth animation for digital characters.

Connected through hyperedge HE-1 (induced by source paper "fashion minimal edits for outfit improvement"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "learning type aware embeddings for fashion compatibility"
    - "unsupervised person image synthesis in arbitrary poses"
    - "toward characteristic preserving image based virtual try on network"
    - "everybody dance now"
    - "viton an image based virtual try on network"
    - "texturegan controlling deep image synthesis with texture patches"
    - "creating capsule wardrobes from fashion images"

Connected through hyperedge HE-2 (induced by source paper "video to video synthesis"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "cgans with projection discriminator"
    - "pose guided human video generation"
    - "synthesizing images of humans in unseen poses"
    - "deep video based performance cloning"
    - "multimodal unsupervised image to image translation"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "world models"

Connected through hyperedge HE-3 (induced by source paper "m2e try on net fashion from model to everyone"):
  with members:
    - "soft gated warping gan for pose guided person image synthesis"
    - "toward characteristic preserving image based virtual try on network"
    - "keypoint based weakly supervised human parsing"
    - "dense pose transfer"
    - "densepose dense human pose estimation in the wild"
    - "viton an image based virtual try on network"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "pose guided person image generation"

Connected through hyperedge HE-4 (induced by source paper "learning a shared shape space for multimodal garment design"):
  with members:
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "unite the people closing the loop between 3d and 2d human representations"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "viton an image based virtual try on network"):
  with members:
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "automatic spatially aware fashion concept discovery"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning fashion compatibility with bidirectional lstms"
    - "fashion forward forecasting visual style in fashion"
    - "photographic image synthesis with cascaded refinement networks"

Connected through hyperedge HE-6 (induced by source paper "be your own prada fashion synthesis with structural coherence"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "pixel level domain transfer"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "generative image modeling using style and structure adversarial networks"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"
    - "image to image translation with conditional adversarial networks"

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)