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 "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.

Includes members:
  Member "sphereface deep hypersphere embedding for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "temporal non volume preserving approach to facial age progression and age invariant face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vec2face unveil human faces from their blackbox features in face recognition")
  Member "face aging with conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithmic fairness")
  Member "learning face age progression a pyramid architecture of gans":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "deep regression forests for age estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust regression via deep negative correlation learning")
  Member "longitudinal face modeling via temporal deep restricted boltzmann machines":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "seesawfacenets sparse and robust face verification model for mobile platform")
  Member "guided cost learning deep inverse optimal control via policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")

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)