human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "global and local consistent age generative adversarial networks"):
  Abstract: Age progression/regression is a challenging task due to the complicated and non-linear transformation in human aging process. Many researches have shown that both global and local facial features are essential for face representation, but previous GAN based methods mainly focused on the global feature in age synthesis. To utilize both global and local facial information, we propose a Global and Local Consistent Age Generative Adversarial Network (GLCA-GAN). In our generator, a global network learns the whole facial structure and simulates the aging trend of the whole face, while three crucial facial patches are progressed or regressed by three local networks aiming at imitating subtle changes of crucial facial subregions. To preserve most of the details in age-attribute-irrelevant areas, our generator learns the residual face. Moreover, we employ an identity preserving loss to better preserve the identity information, as well as age preserving loss to enhance the accuracy of age synthesis. A pixel loss is also adopted to preserve detailed facial information of the input face. Our proposed method is evaluated on three face aging datasets, i.e., CACD dataset, Morph dataset and FG-NET dataset. Experimental results show appealing performance of the proposed method by comparing with the state-of-the-art.

Includes members:
  Member "face aging with contextual generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ugan untraceable gan for multi domain face translation")
  Member "learning from longitudinal face demonstration where tractable deep modeling meets inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vec2face unveil human faces from their blackbox features in face recognition")
  Member "temporal non volume preserving approach to facial age progression and age invariant face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vec2face unveil human faces from their blackbox features in face recognition")
  Member "personalized and occupational aware age progression by generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "global and local consistent wavelet domain age synthesis")
  Member "learning face age progression a pyramid architecture of gans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "child face age progression via deep feature aging")
  Member "age progression regression by conditional adversarial autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "learning from simulated and unsupervised images through adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "longitudinal face modeling via temporal deep restricted boltzmann machines":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "seesawfacenets sparse and robust face verification model for mobile platform")

Question: Please tell me which class the source paper 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)