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: "temporal non volume preserving approach to facial age progression and age invariant face recognition"
  Abstract: Modeling the long-term facial aging process is extremely challenging due to the presence of large and non-linear variations during the face development stages. In order to efficiently address the problem, this work first decomposes the aging process into multiple short-term stages. Then, a novel generative probabilistic model, named Temporal Non-Volume Preserving (TNVP) transformation, is presented to model the facial aging process at each stage. Unlike Generative Adversarial Networks (GANs), which requires an empirical balance threshold, and Restricted Boltzmann Machines (RBM), an intractable model, our proposed TNVP approach guarantees a tractable density function, exact inference and evaluation for embedding the feature transformations between faces in consecutive stages. Our model shows its advantages not only in capturing the non-linear age related variance in each stage but also producing a smooth synthesis in age progression across faces. Our approach can model any face in the wild provided with only four basic landmark points. Moreover, the structure can be transformed into a deep convolutional network while keeping the advantages of probabilistic models with tractable log-likelihood density estimation. Our method is evaluated in both terms of synthesizing age-progressed faces and cross-age face verification and consistently shows the state-of-the-art results in various face aging databases, i.e. FG-NET, MORPH, AginG Faces in the Wild (AGFW), and Cross-Age Celebrity Dataset (CACD). A large-scale face verification on Megaface challenge 1 is also performed to further show the advantages of our proposed approach.

Connected through hyperedge HE-1 (induced by source paper "vec2face unveil human faces from their blackbox features in face recognition"):
  with members:
    - "black box adversarial attacks with bayesian optimization"
    - "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "black box adversarial attacks with limited queries and information"
    - "constructing unrestricted adversarial examples with generative models"

Connected through hyperedge HE-2 (induced by source paper "child face age progression via deep feature aging"):
  with members:
    - "decorrelated adversarial learning for age invariant face recognition"
    - "orthogonal deep features decomposition for age invariant face recognition"
    - "deep convolutional neural networks in the face of caricature identity and image revealed"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "face aging with conditional generative adversarial networks"
    - "learning face age progression a pyramid architecture of gans"

Connected through hyperedge HE-3 (induced by source paper "seesawfacenets sparse and robust face verification model for mobile platform"):
  with members:
    - "seesaw net convolution neural network with uneven group convolution"
    - "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"
    - "searching for mobilenetv3"
    - "airface lightweight and efficient model for face recognition"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep recurrent level set for segmenting brain tumors"
    - "cosface large margin cosine loss for deep face recognition"

Connected through hyperedge HE-4 (induced by source paper "a3gan an attribute aware attentive generative adversarial network for face aging"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "attribute aware face aging with wavelet based generative adversarial networks"
    - "global and local consistent age generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "personalized age progression with bi level aging dictionary learning"
    - "reverse attention for salient object detection"
    - "cbam convolutional block attention module"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "decorrelated adversarial learning for age invariant face recognition"):
  with members:
    - "orthogonal deep features decomposition for age invariant face recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "super identity convolutional neural network for face hallucination"
    - "exploring disentangled feature representation beyond face identification"
    - "additive margin softmax for face verification"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "normface l 2 hypersphere embedding for face verification"

Connected through hyperedge HE-6 (induced by source paper "learning continuous face age progression a pyramid of gans"):
  with members:
    - "face aging with contextual generative adversarial nets"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "learning face age progression a pyramid architecture of gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised cross domain image generation"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "longitudinal face modeling via temporal deep restricted boltzmann machines"
    - "improved techniques for training gans"

Connected through hyperedge HE-7 (induced by source paper "finding missing children aging deep face features"):
  with members:
    - "how are attributes expressed in face dcnns"
    - "decorrelated adversarial learning for age invariant face recognition"
    - "orthogonal deep features decomposition for age invariant face recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "deep convolutional neural networks in the face of caricature identity and image revealed"
    - "look across elapse disentangled representation learning and photorealistic cross age face synthesis for age invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-8 (induced by source paper "recognition in unseen domains domain generalization via universal non volume preserving models"):
  with members:
    - "generalizing across domains via cross gradient training"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "image to image translation for domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "range loss for deep face recognition with long tail"
    - "densely connected convolutional networks"
    - "coupled generative adversarial networks"

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)