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: "synthesis of high quality visible faces from polarimetric thermal faces using generative adversarial networks"
  Abstract: The large domain discrepancy between faces captured in polarimetric (or conventional) thermal and visible domain makes cross-domain face verification a highly challenging problem for human examiners as well as computer vision algorithms. Previous approaches utilize either a two-step procedure (visible feature estimation and visible image reconstruction) or an input-level fusion technique, where different Stokes images are concatenated and used as a multi-channel input to synthesize the visible image given the corresponding polarimetric signatures. Although these methods have yielded improvements, we argue that input-level fusion alone may not be sufficient to realize the full potential of the available Stokes images. We propose a Generative Adversarial Networks (GAN) based multi-stream feature-level fusion technique to synthesize high-quality visible images from prolarimetric thermal images. The proposed network consists of a generator sub-network, constructed using an encoder-decoder network based on dense residual blocks, and a multi-scale discriminator sub-network. The generator network is trained by optimizing an adversarial loss in addition to a perceptual loss and an identity preserving loss to enable photo realistic generation of visible images while preserving discriminative characteristics. An extended dataset consisting of polarimetric thermal facial signatures of 111 subjects is also introduced. Multiple experiments evaluated on different experimental protocols demonstrate that the proposed method achieves state-of-the-art performance. Code will be made available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "polarimetric thermal to visible face verification via self attention guided synthesis"):
  with members:
    - "polarimetric thermal to visible face verification via attribute preserved synthesis"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "spectral normalization for generative adversarial networks"
    - "thermal to visible synthesis of face images using multiple regions"
    - "deep cross polarimetric thermal to visible face recognition"
    - "began boundary equilibrium generative adversarial networks"
    - "attention is all you need"
    - "high quality facial photo sketch synthesis using multi adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "a high efficiency framework for constructing large scale face parsing benchmark"):
  with members:
    - "devil in the details towards accurate single and multiple human parsing"
    - "look at boundary a boundary aware face alignment algorithm"
    - "style aggregated network for facial landmark detection"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "face parsing via recurrent propagation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "stacked hourglass networks for human pose estimation"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "dual variational generation for low shot heterogeneous face recognition"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "high fidelity face manipulation with extreme pose and expression"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "arcface additive angular margin loss for deep face recognition"
    - "delta encoder an effective sample synthesis method for few shot object recognition"
    - "disentangled variational representation for heterogeneous face recognition"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "joint transmission map estimation and dehazing using deep networks"):
  with members:
    - "residual non local attention networks for image restoration"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "semantic foggy scene understanding with synthetic data"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "identity preserving face completion for large ocular region occlusion"
    - "free form image inpainting with gated convolution"
    - "siclope silhouette based clothed people"
    - "gated context aggregation network for image dehazing and deraining"

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)