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: "disentangled variational representation for heterogeneous face recognition"
  Abstract: Visible (VIS) to near infrared (NIR) face matching is a challenging problem due to the significant domain discrepancy between the domains and a lack of sufficient data for training cross-modal matching algorithms. Existing approaches attempt to tackle this problem by either synthesizing visible faces from NIR faces, extracting domain-invariant features from these modalities, or projecting heterogeneous data onto a common latent space for cross-modal matching. In this paper, we take a different approach in which we make use of the Disentangled Variational Representation (DVR) for cross-modal matching. First, we model a face representation with an intrinsic identity information and its within-person variations. By exploring the disentangled latent variable space, a variational lower bound is employed to optimize the approximate posterior for NIR and VIS representations. Second, aiming at obtaining more compact and discriminative disentangled latent space, we impose a minimization of the identity information for the same subject and a relaxed correlation alignment constraint between the NIR and VIS modality variations. An alternative optimization scheme is proposed for the disentangled variational representation part and the heterogeneous face recognition network part. The mutual promotion between these two parts effectively reduces the NIR and VIS domain discrepancy and alleviates over-fitting. Extensive experiments on three challenging NIR-VIS heterogeneous face recognition databases demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "high fidelity face manipulation with extreme pose and expression"):
  with members:
    - "pose agnostic cross spectral hallucination via disentangling independent factors"
    - "generating diverse high fidelity images with vq vae 2"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "semantic image synthesis with spatially adaptive normalization"
    - "dual variational generation for low shot heterogeneous face recognition"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "video to video synthesis"
    - "global and local consistent wavelet domain age synthesis"

Connected through hyperedge HE-2 (induced by source paper "uva a universal variational framework for continuous age analysis"):
  with members:
    - "global and local consistent wavelet domain age synthesis"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "global and local consistent age generative adversarial networks"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "quantifying facial age by posterior of age comparisons"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning face age progression a pyramid architecture of gans"

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"
    - "synthesis of high quality visible faces from polarimetric thermal faces using generative adversarial networks"
    - "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 "pose agnostic cross spectral hallucination via disentangling independent factors"):
  with members:
    - "high fidelity face manipulation with extreme pose and expression"
    - "dual variational generation for low shot heterogeneous face recognition"
    - "multimodal unsupervised image to image translation"
    - "a style based generator architecture for generative adversarial networks"
    - "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"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "lamp hq a large scale multi pose high quality database for nir vis face recognition"):
  with members:
    - "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"
    - "a light cnn for deep face representation with noisy labels"

Connected through hyperedge HE-6 (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"
    - "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"

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)