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: "generative adversarial network based synthesis of visible faces from polarimetric thermal faces"
  Abstract: The large domain discrepancy between faces captured in polarimetric (or conventional) thermal and visible domain makes cross-domain face recognition quite a challenging problem for both human-examiners and computer vision algorithms. Previous approaches utilize a two-step procedure (visible feature estimation and visible image reconstruction) to synthesize the visible image given the corresponding polarimetric thermal image. However, these are regarded as two disjoint steps and hence may hinder the performance of visible face reconstruction. We argue that joint optimization would be a better way to reconstruct more photo-realistic images for both computer vision algorithms and human-examiners to examine. To this end, this paper proposes a Generative Adversarial Network-based Visible Face Synthesis (GAN-VFS) method to synthesize more photo-realistic visible face images from their corresponding polarimetric images. To ensure that the encoded visible-features contain more semantically meaningful information in reconstructing the visible face image, a guidance sub-network is involved into the training procedure. To achieve photo realistic property while preserving discriminative characteristics for the reconstructed outputs, an identity loss combined with the perceptual loss are optimized in the framework. Multiple experiments evaluated on different experimental protocols demonstrate that the proposed method achieves state-of-the-art performance.

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"
    - "synthesis of high quality visible faces from polarimetric thermal faces using generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "attribute guided deep polarimetric thermal to visible face recognition"):
  with members:
    - "toward marker free 3d pose estimation in lifting a deep multi view solution"
    - "perceptual losses for real time style transfer and super resolution"
    - "face attribute prediction using off the shelf cnn features"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi task cnn model for attribute prediction"
    - "deep perceptual mapping for thermal to visible face recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "cascaded generation of high quality color visible face images from thermal captures"):
  with members:
    - "thermal to visible synthesis of face images using multiple regions"
    - "tv gan generative adversarial network based thermal to visible face recognition"
    - "photographic image synthesis with cascaded refinement networks"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep perceptual mapping for thermal to visible face recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "synthesis of high quality visible faces from polarimetric thermal faces using generative adversarial networks"):
  with members:
    - "polarimetric thermal to visible face verification via attribute preserved synthesis"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deep cross polarimetric thermal to visible face recognition"
    - "disentangled variational representation for heterogeneous face recognition"
    - "context encoding for semantic segmentation"
    - "densely connected pyramid dehazing network"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-5 (induced by source paper "deep texture manifold for ground terrain recognition"):
  with members:
    - "island loss for learning discriminative features in facial expression recognition"
    - "exemplar centered supervised shallow parametric data embedding"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "hidden two stream convolutional networks for action recognition"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "deep ten texture encoding network"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "depth2action exploring embedded depth for large scale action recognition"

Connected through hyperedge HE-6 (induced by source paper "toward marker free 3d pose estimation in lifting a deep multi view solution"):
  with members:
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "image de raining using a conditional generative adversarial network"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "learning to fuse 2d and 3d image cues for monocular body pose estimation"

Connected through hyperedge HE-7 (induced by source paper "disentangled variational representation for heterogeneous face recognition"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "coupled deep learning for heterogeneous face recognition"
    - "adversarial discriminative heterogeneous face recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "wasserstein cnn learning invariant features for nir vis face recognition"
    - "not afraid of the dark nir vis face recognition via cross spectral hallucination and low rank embedding"
    - "deep coral correlation alignment for deep domain adaptation"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"

Connected through hyperedge HE-8 (induced by source paper "thermal to visible synthesis of face images using multiple regions"):
  with members:
    - "image to image translation with conditional adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep perceptual mapping for thermal to visible face recognition"
    - "understanding deep image representations by inverting them"

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)