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 cross modal face recognition"
  Abstract: Cross modal face matching between the thermal and visible spectrum is a much desired 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 between the two modalities by using a deep neural network. Our model attempts to learn a non-linear mapping from the visible to the thermal spectrum while preserving the identity information. We show substantive performance improvement on three difficult thermal---visible face datasets. The presented approach improves the state-of-the-art by more than 10 % on the UND-X1 dataset and by more than 15---30 % on the NVESD dataset in terms of Rank-1 identification. Our method bridges the drop in performance due to the modality gap by more than 40 %.

Connected through hyperedge HE-1 (induced by source paper "hi cmd hierarchical cross modality disentanglement for visible infrared person re identification"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "unsupervised person image generation with semantic parsing transformation"
    - "illumination adaptive person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "gait recognition via disentangled representation learning"
    - "decorrelated adversarial learning for age invariant face recognition"
    - "re identification with consistent attentive siamese networks"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-2 (induced by source paper "cross spectral face completion for nir vis heterogeneous face recognition"):
  with members:
    - "wasserstein cnn learning invariant features for nir vis face recognition"

Connected through hyperedge HE-3 (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-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "tv gan generative adversarial network based thermal to visible face recognition"):
  with members:
    - "generative adversarial network based synthesis of visible faces from polarimetric thermal faces"
    - "perceptual losses for real time style transfer and super resolution"
    - "generative image modeling using style and structure adversarial networks"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "densely connected convolutional networks"
    - "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"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "geometric cross modal comparison of heterogeneous sensor data"):
  with members:
    - "cover song identification with timbral shape sequences"
    - "topological and statistical behavior classifiers for tracking applications"

Connected through hyperedge HE-7 (induced by source paper "sparse graphical representation based discriminant analysis for heterogeneous face recognition"):
  with members:
    - "coupled deep learning for heterogeneous face recognition"
    - "adversarial discriminative heterogeneous face recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "viplfacenet an open source deep face recognition sdk"
    - "graphical representation for heterogeneous face recognition"
    - "end to end photo sketch generation via fully convolutional representation learning"
    - "shared representation learning for heterogeneous face recognition"

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)