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: "a cyclically trained adversarial network for invariant representation learning"
  Abstract: We propose a cyclically-trained adversarial network to learn mappings from image space to a latent representation space and back such that the latent representation is invariant to a specified factor of variation (e.g., identity). The learned mappings also assure that the synthesized image is not only realistic, but has the same values for unspecified factors (e.g., pose and illumination) as the original image and a desired value of the specified factor. We encourage invariance to a specified factor, by applying adversarial training using a variational autoencoder in the image space as opposed to the latent space. We strengthen this invariance by introducing a cyclic training process (forward and backward pass). We also propose a new method to evaluate conditional generative networks. It compares how well different factors of variation can be predicted from the synthesized, as opposed to real, images. We demonstrate the effectiveness of our approach on factors such as identity, pose, illumination or style on three datasets and compare it with state-of-the-art methods. Our network produces good quality synthetic images and, interestingly, can be used to perform face morphing in latent space.

Connected through hyperedge HE-1 (induced by source paper "shadow transfer single image relighting for urban road scenes"):
  with members:
    - "an adversarial neuro tensorial approach for learning disentangled representations"
    - "failing to learn autonomously identifying perception failures for self driving cars"
    - "online illumination invariant moving object detection by generative neural network"
    - "night to day image translation for retrieval based localization"
    - "geometric image synthesis"
    - "carla an open urban driving simulator"
    - "combogan unrestrained scalability for image domain translation"
    - "interpretable transformations with encoder decoder networks"

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)