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: "learning from longitudinal face demonstration where tractable deep modeling meets inverse reinforcement learning"
  Abstract: This paper presents a novel Generative Probabilistic Modeling under an Inverse Reinforcement Learning approach, named Subject-dependent Deep Aging Path (SDAP), to model the facial structures and the longitudinal face aging process of given subjects. The proposed SDAP is optimized using tractable log-likelihood objective functions with Convolutional Neural Networks based deep feature extraction. In addition, instead of using a fixed aging development path for all input faces and subjects, SDAP is able to provide the most appropriate aging development path for each subject that optimizes the reward aging formulation. Unlike previous methods that can take only one image as the input, SDAP allows multiple images as inputs, i.e. all information of a subject at either the same or different ages, to produce the optimal aging path for the subject. Finally, SDAP allows efficiently synthesizing in-the-wild aging faces without a complicated pre-processing step. The proposed method is experimented in both tasks of face aging synthesis and cross-age face verification. The experimental results consistently show the state-of-the-art performance using SDAP on numerous face aging databases, i.e. FG-NET, MORPH, AginG Faces in the Wild (AGFW), and Cross-Age Celebrity Dataset (CACD). The method also performs on the large-scale Megaface challenge 1 to demonstrate the advantages of the proposed solution.

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 "global and local consistent age generative adversarial networks"):
  with members:
    - "face aging with contextual generative adversarial nets"
    - "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"
    - "longitudinal face modeling via temporal deep restricted boltzmann machines"
    - "face aging effect simulation using hidden factor analysis joint sparse representation"

Connected through hyperedge HE-3 (induced by source paper "longitudinal face aging in the wild recent deep learning approaches"):
  with members:
    - "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"
    - "age progression regression by conditional adversarial autoencoder"
    - "longitudinal face modeling via temporal deep restricted boltzmann machines"
    - "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)