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: "deep cross polarimetric thermal to visible face recognition"
  Abstract: In this paper, we present a deep coupled learning frame- work to address the problem of matching polarimetric ther- mal face photos against a gallery of visible faces. Polariza- tion state information of thermal faces provides the miss- ing textural and geometrics details in the thermal face im- agery which exist in visible spectrum. we propose a coupled deep neural network architecture which leverages relatively large visible and thermal datasets to overcome the problem of overfitting and eventually we train it by a polarimetric thermal face dataset which is the first of its kind. The pro- posed architecture is able to make full use of the polari- metric thermal information to train a deep model compared to the conventional shallow thermal-to-visible face recogni- tion methods. Proposed coupled deep neural network also finds global discriminative features in a nonlinear embed- ding space to relate the polarimetric thermal faces to their corresponding visible faces. The results show the superior- ity of our method compared to the state-of-the-art models in cross thermal-to-visible face recognition algorithms.

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"
    - "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"
    - "high quality facial photo sketch synthesis using multi adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "matching thermal to visible face images using a semantic guided generative adversarial network"):
  with members:
    - "polarimetric thermal to visible face verification via attribute preserved synthesis"
    - "the gan landscape losses architectures regularization and normalization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "thermal to visible synthesis of face images using multiple regions"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "additive margin softmax for face verification"
    - "deep semantic face deblurring"
    - "macro micro adversarial network for human parsing"

Connected through hyperedge HE-3 (induced by source paper "automatic health problem detection from gait videos using deep neural networks"):
  with members:
    - "toward marker free 3d pose estimation in lifting a deep multi view solution"
    - "deep sketch photo face recognition assisted by facial attributes"
    - "harvesting multiple views for marker less 3d human pose annotations"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "learning to fuse 2d and 3d image cues for monocular body pose estimation"

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"
    - "disentangled variational representation for heterogeneous face recognition"
    - "context encoding for semantic segmentation"
    - "densely connected pyramid dehazing network"
    - "generative image inpainting with contextual attention"
    - "generating high quality crowd density maps using contextual pyramid cnns"

Connected through hyperedge HE-5 (induced by source paper "id preserving generative adversarial network for partial latent fingerprint reconstruction"):
  with members:
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "fingerprint distortion rectification using deep convolutional neural networks"
    - "generalized bilinear deep convolutional neural networks for multimodal biometric identification"
    - "generative convolutional networks for latent fingerprint reconstruction"
    - "perceptual losses for real time style transfer and super resolution"
    - "latent fingerprint minutia extraction using fully convolutional network"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "deep sketch photo face recognition assisted by facial attributes"):
  with members:
    - "attribute centered loss for soft biometrics guided face sketch photo recognition"
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "fingerprint distortion rectification using deep convolutional neural networks"
    - "generalized bilinear deep convolutional neural networks for multimodal biometric identification"
    - "3d convolutional neural networks for cross audio visual matching recognition"
    - "face attribute prediction using off the shelf cnn features"
    - "sparse graphical representation based discriminant analysis for heterogeneous face recognition"
    - "moon a mixed objective optimization network for the recognition of facial attributes"

Connected through hyperedge HE-7 (induced by source paper "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"):
  with members:
    - "attribute centered loss for soft biometrics guided face sketch photo recognition"
    - "fingerprint distortion rectification using deep convolutional neural networks"
    - "generalized bilinear deep convolutional neural networks for multimodal biometric identification"
    - "few shot adversarial domain adaptation"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "the nd iris 0405 iris image dataset"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "an extensive review on spectral imaging in biometric systems challenges and advancements"):
  with members:
    - "a novel database of children s spontaneous facial expressions liris cse"
    - "what do we understand about convolutional networks"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "deep perceptual mapping for cross modal face recognition"
    - "image level classification in hyperspectral images using feature descriptors with application to face recognition"
    - "not afraid of the dark nir vis face recognition via cross spectral hallucination and low rank embedding"
    - "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)