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: "random sampling for fast face sketch synthesis"
  Abstract: Exemplar-based face sketch synthesis plays an important role in both digital entertainment and law enforcement. It generally consists of two parts: neighbor selection and reconstruction weight representation. The most time-consuming or main computation complexity for exemplar-based face sketch synthesis methods lies in the neighbor selection process. State-of-the-art face sketch synthesis methods perform neighbor selection online in a data-driven manner by $K$ nearest neighbor ($K$-NN) searching. Actually, the online search increases the time consuming for synthesis. Moreover, since these methods need to traverse the whole training dataset for neighbor selection, the computational complexity increases with the scale of the training database and hence these methods have limited scalability. In this paper, we proposed a simple but effective offline random sampling in place of online $K$-NN search to improve the synthesis efficiency. Extensive experiments on public face sketch databases demonstrate the superiority of the proposed method in comparison to state-of-the-art methods, in terms of both synthesis quality and time consumption. The proposed method could be extended to other heterogeneous face image transformation problems such as face hallucination. We release the source codes of our proposed methods and the evaluation metrics for future study online: this http URL.

Connected through hyperedge HE-1 (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"
    - "structure measure a new way to evaluate foreground maps"

Connected through hyperedge HE-2 (induced by source paper "semi supervised learning for face sketch synthesis in the wild"):
  with members:
    - "random sampling and locality constraint for face sketch"
    - "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"
    - "combining markov random fields and convolutional neural networks for image synthesis"

Connected through hyperedge HE-3 (induced by source paper "face sketch synthesis style similarity a new structure co occurrence texture measure"):
  with members:
    - "structure measure a new way to evaluate foreground maps"
    - "end to end photo sketch generation via fully convolutional representation learning"
    - "gradient magnitude similarity deviation a highly efficient perceptual image quality index"

Connected through hyperedge HE-4 (induced by source paper "instance based entropy fuzzy support vector machine for imbalanced data"):
  with members:
    - "data augmentation by pairing samples for images classification"
    - "extreme entropy machines robust information theoretic classification"

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"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "stackgan realistic image synthesis with stacked generative 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)