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 and locality constraint for face sketch"
  Abstract: 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. State-of-the-art face sketch synthesis methods perform neighbor selection online in a data-driven manner by $K$ nearest neighbor ($K$-NN) searching. However, 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 addition, state-of-the-art methods consider that all selected neighbors contribute equally to the reconstruction weight computation process while the distinct similarity between the test patch and these neighbors are neglected. In this paper, we employ offline random sampling in place of online $K$-NN search to improve the synthesis efficiency. Locality constraint is introduced to model the distinct correlations between the test patch and random sampled patches. 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 will 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 "semi supervised learning for face sketch synthesis in the wild"):
  with members:
    - "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"
    - "combining markov random fields and convolutional neural networks for image synthesis"

Connected through hyperedge HE-2 (induced by source paper "recent progress of face image synthesis"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "generative face completion"
    - "face aging with conditional generative adversarial networks"
    - "srefi synthesis of realistic example face images"
    - "genegan learning object transfiguration and attribute subspace from unpaired data"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "multi view image generation from a single view"
    - "lr gan layered recursive generative adversarial networks for image generation"

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)