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: "r btn cross domain face composite and synthesis from limited facial patches"
  Abstract: We start by asking an interesting yet challenging question, "If an eyewitness can only recall the eye features of the suspect, such that the forensic artist can only produce a sketch of the eyes (e.g., the top-left sketch shown in Fig. 1), can advanced computer vision techniques help generate the whole face image?" A more generalized question is that if a large proportion (e.g., more than 50%) of the face/sketch is missing, can a realistic whole face sketch/image still be estimated. Existing face completion and generation methods either do not conduct domain transfer learning or can not handle large missing area. For example, the inpainting approach tends to blur the generated region when the missing area is large (i.e., more than 50%). In this paper, we exploit the potential of deep learning networks in filling large missing region (e.g., as high as 95% missing) and generating realistic faces with high-fidelity in cross domains. We propose the recursive generation by bidirectional transformation networks (r-BTN) that recursively generates a whole face/sketch from a small sketch/face patch. The large missing area and the cross domain challenge make it difficult to generate satisfactory results using a unidirectional cross-domain learning structure. On the other hand, a forward and backward bidirectional learning between the face and sketch domains would enable recursive estimation of the missing region in an incremental manner (Fig. 1) and yield appealing results. r-BTN also adopts an adversarial constraint to encourage the generation of realistic faces/sketches. Extensive experiments have been conducted to demonstrate the superior performance from r-BTN as compared to existing potential solutions.

Connected through hyperedge HE-1 (induced by source paper "attribute guided sketch generation"):
  with members:
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "instagan instance aware image to image translation"
    - "deformable gans for pose based human image generation"
    - "image generation from sketch constraint using contextual gan"
    - "unsupervised creation of parameterized avatars"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "semi supervised learning with context conditional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "a layer based sequential framework for scene generation with gans"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "mc gan multi conditional generative adversarial network for image synthesis"
    - "spectral normalization for generative adversarial networks"
    - "image generation from scene graphs"
    - "exprgan facial expression editing with controllable expression intensity"
    - "lr gan layered recursive generative adversarial networks for image generation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "talking face generation by conditional recurrent adversarial network"):
  with members:
    - "image super resolution by neural texture transfer"
    - "video to video synthesis"
    - "decoupled learning for conditional adversarial networks"
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "dual motion gan for future flow embedded video prediction"
    - "decomposing motion and content for natural video sequence prediction"
    - "obamanet photo realistic lip sync from text"

Connected through hyperedge HE-4 (induced by source paper "decoupled learning for conditional adversarial networks"):
  with members:
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "age progression regression by conditional adversarial autoencoder"
    - "nips 2016 tutorial generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "unrolled generative adversarial networks"
    - "improved techniques for training gans"

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)