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: "fast preprocessing for robust face sketch synthesis"
  Abstract: Exemplar-based face sketch synthesis methods usually meet the challenging problem that input photos are captured in different lighting conditions from training photos. The critical step causing the failure is the search of similar patch candidates for an input photo patch. Conventional illumination invariant patch distances are adopted rather than directly relying on pixel intensity difference, but they will fail when local contrast within a patch changes. In this paper, we propose a fast preprocessing method named Bidirectional Luminance Remapping (BLR), which interactively adjust the lighting of training and input photos. Our method can be directly integrated into state-of-the-art exemplar-based methods to improve their robustness with ignorable computational cost.

Connected through hyperedge HE-1 (induced by source paper "attentive ct lesion detection using deep pyramid inference with multi scale booster"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "joint face hallucination and deblurring via structure generation and detail enhancement"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "squeeze and excitation networks"
    - "deeplesion automated deep mining categorization and detection of significant radiology image findings using large scale clinical lesion annotations"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (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"
    - "stylizing face images via multiple exemplars"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "vital visual tracking via adversarial learning"):
  with members:
    - "stylizing face images via multiple exemplars"
    - "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"
    - "a novel performance evaluation methodology for single target trackers"

Connected through hyperedge HE-4 (induced by source paper "image correction via deep reciprocating hdr transformation"):
  with members:
    - "stylizing face images via multiple exemplars"
    - "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"

Connected through hyperedge HE-5 (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-6 (induced by source paper "random sampling for fast face sketch synthesis"):
  with members:
    - "stylizing face images via multiple exemplars"
    - "learning to hallucinate face images via component generation and enhancement"
    - "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)