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: "matching thermal to visible face images using a semantic guided generative adversarial network"
  Abstract: Designing face recognition systems that are capable of matching face images obtained in the thermal spectrum with those obtained in the visible spectrum is a challenging problem. In this work, we propose the use of semantic-guided generative adversarial network (SG-GAN) to automatically synthesize visible face images from their thermal counterparts. Specifically, semantic labels, extracted by a face parsing network, are used to compute a semantic loss function to regularize the adversarial network during training. These semantic cues denote high-level facial component information associated with each pixel. Further, an identity extraction network is leveraged to generate multi-scale features to compute an identity loss function. To achieve photo-realistic results, a perceptual loss function is introduced during network training to ensure that the synthesized visible face is perceptually similar to the target visible face image. We extensively evaluate the benefits of individual loss functions, and combine them effectively to learn the mapping from thermal to visible face images. Experiments involving two multispectral face datasets show that the proposed method achieves promising results in both face synthesis and cross-spectral face matching.

Connected through hyperedge HE-1 (induced by source paper "spectrum translation for cross spectral ocular matching"):
  with members:
    - "deep representations for cross spectral ocular biometrics"
    - "a survey on periocular biometrics research"
    - "cross spectral periocular recognition a survey"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "thermal to visible face recognition using deep autoencoders"):
  with members:
    - "tv gan generative adversarial network based thermal to visible face recognition"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "mask embedding in conditional gan for guided synthesis of high resolution images"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "high resolution mammogram synthesis using progressive generative adversarial networks"
    - "disentangling multiple conditional inputs in gans"
    - "progressive growing of gans for improved quality stability and variation"
    - "high resolution deep convolutional generative adversarial networks"
    - "face aging with conditional generative adversarial networks"
    - "improved training of wasserstein gans"

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)