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: "longitudinal face modeling via temporal deep restricted boltzmann machines"
  Abstract: Modeling the face aging process is a challenging task due to large and non-linear variations present in different stages of face development. This paper presents a deep model approach for face age progression that can efficiently capture the non-linear aging process and automatically synthesize a series of age-progressed faces in various age ranges. In this approach, we first decompose the long-term age progress into a sequence of short-term changes and model it as a face sequence. The Temporal Deep Restricted Boltzmann Machines based age progression model together with the prototype faces are then constructed to learn the aging transformation between faces in the sequence. In addition, to enhance the wrinkles of faces in the later age ranges, the wrinkle models are further constructed using Restricted Boltzmann Machines to capture their variations in different facial regions. The geometry constraints are also taken into account in the last step for more consistent age-progressed results. The proposed approach is evaluated using various face aging databases, i.e. FG-NET, Cross-Age Celebrity Dataset (CACD) and MORPH, and our collected large-scale aging database named AginG Faces in the Wild (AGFW). In addition, when ground-truth age is not available for input image, our proposed system is able to automatically estimate the age of the input face before aging process is employed.

Connected through hyperedge HE-1 (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-2 (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-3 (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"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "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"
    - "improved techniques for training gans"

Connected through hyperedge HE-4 (induced by source paper "mobiface a lightweight deep learning face recognition on mobile devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep recurrent level set for segmenting brain tumors"
    - "cosface large margin cosine loss for deep face recognition"
    - "seeing small faces from robust anchor s perspective"
    - "quantization mimic towards very tiny cnn for object detection"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "fd mobilenet improved mobilenet with a fast downsampling strategy"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-5 (induced by source paper "global and local consistent wavelet domain age synthesis"):
  with members:
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "face aging with contextual generative adversarial nets"
    - "global and local consistent age generative adversarial networks"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "personalized and occupational aware age progression by generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "learning face age progression a pyramid architecture of gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "attribute aware face aging with wavelet based generative adversarial networks"):
  with members:
    - "age progression and regression with spatial attention modules"
    - "global and local consistent age generative adversarial networks"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "face aging with conditional generative adversarial networks"
    - "learning face age progression a pyramid architecture of gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "age progression regression by conditional adversarial autoencoder"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (induced by source paper "global and local consistent age generative adversarial networks"):
  with members:
    - "face aging with contextual generative adversarial nets"
    - "learning from longitudinal face demonstration where tractable deep modeling meets inverse reinforcement learning"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "personalized and occupational aware age progression by generative adversarial networks"
    - "learning face age progression a pyramid architecture of gans"
    - "age progression regression by conditional adversarial autoencoder"
    - "learning from simulated and unsupervised images through adversarial training"
    - "face aging effect simulation using hidden factor analysis joint sparse representation"

Connected through hyperedge HE-8 (induced by source paper "longitudinal face aging in the wild recent deep learning approaches"):
  with members:
    - "global and local consistent age generative adversarial networks"
    - "learning from longitudinal face demonstration where tractable deep modeling meets inverse reinforcement learning"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "face aging with conditional generative adversarial networks"
    - "age progression regression by conditional adversarial autoencoder"
    - "face aging effect simulation using hidden factor analysis joint sparse representation"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "robust deep appearance models"

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)