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: "personalized and occupational aware age progression by generative adversarial networks"
  Abstract: Face age progression, which aims to predict the future looks, is important for various applications and has been received considerable attentions. Existing methods and datasets are limited in exploring the effects of occupations which may influence the personal appearances. In this paper, we firstly introduce an occupational face aging dataset for studying the influences of occupations on the appearances. It includes five occupations, which enables the development of new algorithms for age progression and facilitate future researches. Second, we propose a new occupational-aware adversarial face aging network, which learns human aging process under different occupations. Two factors are taken into consideration in our aging process: personality-preserving and visually plausible texture change for different occupations. We propose personalized network with personalized loss in deep autoencoder network for keeping personalized facial characteristics, and occupational-aware adversarial network with occupational-aware adversarial loss for obtaining more realistic texture changes. Experimental results well demonstrate the advantages of the proposed method by comparing with other state-of-the-arts age progression methods.

Connected through hyperedge HE-1 (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"
    - "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"
    - "age progression regression by conditional adversarial autoencoder"

Connected through hyperedge HE-2 (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"
    - "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"
    - "longitudinal face modeling via temporal deep restricted boltzmann machines"
    - "face aging effect simulation using hidden factor analysis joint sparse representation"

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)