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: "stylizing face images via multiple exemplars"
  Abstract: Abstract   We address the problem of transferring the style of a headshot photo to face images. Existing methods using a single exemplar lead to inaccurate results when the exemplar does not contain sufficient stylized facial components for a given photo. In this work, we propose an algorithm to stylize face images using multiple exemplars containing different subjects in the same style. Patch correspondences between an input photo and multiple exemplars are established using a Markov Random Field (MRF), which enables accurate local energy transfer via Laplacian stacks. As image patches from multiple exemplars are used, the boundaries of facial components on the target image are inevitably inconsistent. The artifacts are removed by a post-processing step using an edge-preserving filter. Experimental results show that the proposed algorithm consistently produces visually pleasing results.

Connected through hyperedge HE-1 (induced by source paper "joint face hallucination and deblurring via structure generation and detail enhancement"):
  with members:
    - "image correction via deep reciprocating hdr transformation"
    - "deep semantic face deblurring"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "progressive growing of gans for improved quality stability and variation"
    - "pose invariant face alignment with a single cnn"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "attention aware face hallucination via deep reinforcement learning"
    - "dilated residual networks"

Connected through hyperedge HE-2 (induced by source paper "vital visual tracking via adversarial learning"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "focal loss for dense object detection"
    - "crest convolutional residual learning for visual tracking"
    - "learning background aware correlation filters for visual tracking"
    - "learning to hallucinate face images via component generation and enhancement"
    - "fast preprocessing for robust face sketch synthesis"
    - "a novel performance evaluation methodology for single target trackers"

Connected through hyperedge HE-3 (induced by source paper "image correction via deep reciprocating hdr transformation"):
  with members:
    - "msr net low light image enhancement using deep convolutional network"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "learning high dynamic range from outdoor panoramas"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"
    - "learning deep cnn denoiser prior for image restoration"
    - "segflow joint learning for video object segmentation and optical flow"
    - "memnet a persistent memory network for image restoration"
    - "crest convolutional residual learning for visual tracking"

Connected through hyperedge HE-4 (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"
    - "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"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "random sampling for fast face sketch synthesis"):
  with members:
    - "learning to hallucinate face images via component generation and enhancement"
    - "fast preprocessing for robust face sketch synthesis"
    - "image to image translation with conditional adversarial networks"
    - "end to end photo sketch generation via fully convolutional representation learning"

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)