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 "towards large pose face frontalization in the wild"):
  Abstract: Despite recent advances in face recognition using deep learning, severe accuracy drops are observed for large pose variations in unconstrained environments. Learning pose-invariant features is one solution, but needs expensively labeled large-scale data and carefully designed feature learning algorithms. In this work, we focus on frontalizing faces in the wild under various head poses, including extreme profile views. We propose a novel deep 3D Morphable Model (3DMM) conditioned Face Frontalization Generative Adversarial Network (GAN), termed as FF-GAN, to generate neutral head pose face images. Our framework differs from both traditional GANs and 3DMM based modeling. Incorporating 3DMM into the GAN structure provides shape and appearance priors for fast convergence with less training data, while also supporting end-to-end training. The 3DMM-conditioned GAN employs not only the discriminator and generator loss but also a new masked symmetry loss to retain visual quality under occlusions, besides an identity loss to recover high frequency information. Experiments on face recognition, landmark localization and 3D reconstruction consistently show the advantage of our frontalization method on faces in the wild datasets.

Includes members:
  Member "multi task convolutional neural network for pose invariant face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "transformation grounded image generation network for novel 3d view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "reconstruction based disentanglement for pose invariant face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "synthesizing normalized faces from facial identity features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vec2face unveil human faces from their blackbox features in face recognition")
  Member "unsupervised pixel level domain adaptation with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "weakly supervised disentangling with recurrent transformations for 3d view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised disentanglement without compromises")
  Member "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "infogan interpretable representation learning by information maximizing generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")

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)