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 perceptual mapping for thermal to visible face recognition"
  Abstract: Cross modal face matching between the thermal and visible spectrum is a much de- sired capability for night-time surveillance and security applications. Due to a very large modality gap, thermal-to-visible face recognition is one of the most challenging face matching problem. In this paper, we present an approach to bridge this modality gap by a significant margin. Our approach captures the highly non-linear relationship be- tween the two modalities by using a deep neural network. Our model attempts to learn a non-linear mapping from visible to thermal spectrum while preserving the identity in- formation. We show substantive performance improvement on a difficult thermal-visible face dataset. The presented approach improves the state-of-the-art by more than 10% in terms of Rank-1 identification and bridge the drop in performance due to the modality gap by more than 40%.

Connected through hyperedge HE-1 (induced by source paper "relational deep feature learning for heterogeneous face recognition"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep face recognition a survey"
    - "videos as space time region graphs"
    - "cbam convolutional block attention module"
    - "non local neural networks"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-2 (induced by source paper "lamp hq a large scale multi pose high quality database for nir vis face recognition"):
  with members:
    - "disentangled variational representation for heterogeneous face recognition"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "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"
    - "perceptual losses for real time style transfer and super resolution"
    - "not afraid of the dark nir vis face recognition via cross spectral hallucination and low rank embedding"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-3 (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"
    - "generative adversarial network based synthesis of visible faces from polarimetric thermal faces"
    - "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"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (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"
    - "generative adversarial network based synthesis of visible faces from polarimetric thermal faces"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (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-6 (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"
    - "deep cross polarimetric thermal to visible face recognition"
    - "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"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-7 (induced by source paper "deep cross polarimetric thermal to visible face recognition"):
  with members:
    - "learning cross modal deep representations for robust pedestrian detection"
    - "unified deep supervised domain adaptation and generalization"
    - "deepid3 face recognition with very deep neural networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "very deep convolutional networks for large scale image recognition"
    - "shared representation learning for heterogeneous face recognition"
    - "deeply learned face representations are sparse selective and robust"
    - "a survey on heterogeneous face recognition sketch infra red 3d and low resolution"

Connected through hyperedge HE-8 (induced by source paper "thermal to visible synthesis of face images using multiple regions"):
  with members:
    - "generative adversarial network based synthesis of visible faces from polarimetric thermal faces"
    - "image to image translation with conditional adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "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)