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: "end to end photo sketch generation via fully convolutional representation learning"
  Abstract: Sketch-based face recognition is an interesting task in vision and multimedia research, yet it is quite challenging due to the great difference between face photos and sketches. In this paper, we propose a novel approach for photo-sketch generation, aiming to automatically transform face photos into detail-preserving personal sketches. Unlike the traditional models synthesizing sketches based on a dictionary of exemplars, we develop a fully convolutional network to learn the end-to-end photo-sketch mapping. Our approach takes whole face photos as inputs and directly generates the corresponding sketch images with efficient inference and learning, in which the architecture is stacked by only convolutional kernels of very small sizes. To well capture the person identity during the photo-sketch transformation, we define our optimization objective in the form of joint generative discriminative minimization. In particular, a discriminative regularization term is incorporated into the photo-sketch generation, enhancing the discriminability of the generated person sketches against other individuals. Extensive experiments on several standard benchmarks suggest that our approach outperforms other state-of-the-arts in both photo sketch generation and face sketch verification.

Connected through hyperedge HE-1 (induced by source paper "cdgan cyclic discriminative generative adversarial networks for image to image transformation"):
  with members:
    - "csgan cyclic synthesized generative adversarial networks for image to image transformation"
    - "generative adversarial networks and perceptual losses for video super resolution"
    - "recsys dan discriminative adversarial networks for cross domain recommender systems"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "perceptual adversarial networks for image to image transformation"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"

Connected through hyperedge HE-2 (induced by source paper "csgan cyclic synthesized generative adversarial networks for image to image transformation"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "perceptual adversarial networks for image to image transformation"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "toward multimodal image to image translation"
    - "gp gan towards realistic high resolution image blending"
    - "dualgan unsupervised dual learning for image to image translation"

Connected through hyperedge HE-3 (induced by source paper "scoot a perceptual metric for facial sketches"):
  with members:
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "enhanced alignment measure for binary foreground map evaluation"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "a multilayer based framework for online background subtraction with freely moving cameras"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "random sampling for fast face sketch synthesis"

Connected through hyperedge HE-4 (induced by source paper "semi supervised learning for face sketch synthesis in the wild"):
  with members:
    - "random sampling and locality constraint for face sketch"
    - "random sampling for fast face sketch synthesis"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "towards realistic face photo sketch synthesis via composition aided gans"
    - "content adaptive sketch portrait generation by decompositional representation learning"
    - "photo realistic facial texture transfer"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "unsupervised facial geometry learning for sketch to photo synthesis"):
  with members:
    - "image segmentation using sparse subset selection"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "convolutional sketch inversion"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "face sketch synthesis style similarity a new structure co occurrence texture measure"):
  with members:
    - "random sampling for fast face sketch synthesis"
    - "structure measure a new way to evaluate foreground maps"
    - "gradient magnitude similarity deviation a highly efficient perceptual image quality index"

Connected through hyperedge HE-7 (induced by source paper "towards realistic face photo sketch synthesis via composition aided gans"):
  with members:
    - "the perception distortion tradeoff"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "stylizing face images via multiple exemplars"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "focal loss for dense object detection"
    - "random sampling for fast face sketch synthesis"
    - "high quality facial photo sketch synthesis using multi adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "high quality facial photo sketch synthesis using multi adversarial networks"):
  with members:
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised creation of parameterized avatars"
    - "in2i unsupervised multi image to image translation using generative adversarial networks"
    - "towards realistic face photo sketch synthesis via composition aided gans"
    - "image de raining using a conditional generative adversarial network"

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)