human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "thermal to visible synthesis of face images using multiple regions"):
  Abstract: Synthesis of visible spectrum faces from thermal facial imagery is a promising approach for heterogeneous face recognition; enabling existing face recognition software trained on visible imagery to be leveraged, and allowing human analysts to verify cross-spectrum matches more effectively. We propose a new synthesis method to enhance the discriminative quality of synthesized visible face imagery by leveraging both global (e.g., entire face) and local regions (e.g., eyes, nose, and mouth). Here, each region provides (1) an independent representation for the corresponding area, and (2) additional regularization terms, which impact the overall quality of synthesized images. We analyze the effects of using multiple regions to synthesize a visible face image from a thermal face. We demonstrate that our approach improves cross-spectrum verification rates over recently published synthesis approaches. Moreover, using our synthesized imagery, we report the results on facial landmark detection-commonly used for image registration-which is a critical part of the face recognition process.

Includes members:
  Member "generative adversarial network based synthesis of visible faces from polarimetric thermal faces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polarimetric thermal to visible face verification via self attention guided synthesis")
  Member "image to image translation with conditional adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "unsupervised representation learning with deep convolutional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "deep perceptual mapping for thermal to visible face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relational deep feature learning for heterogeneous face recognition")
  Member "understanding deep image representations by inverting them":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crl class representative learning for image classification")

Question: Please tell me which class the source paper 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)