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: "explicit disentanglement of appearance and perspective in generative models"
  Abstract: Disentangled representation learning finds compact, independent and easy-to-interpret factors of the data. Learning such has been shown to require an inductive bias, which we explicitly encode in a generative model of images. Specifically, we propose a model with two latent spaces: one that represents spatial transformations of the input data, and another that represents the transformed data. We find that the latter naturally captures the intrinsic appearance of the data. To realize the generative model, we propose a Variationally Inferred Transformational Autoencoder (VITAE) that incorporates a spatial transformer into a variational autoencoder. We show how to perform inference in the model efficiently by carefully designing the encoders and restricting the transformation class to be diffeomorphic. Empirically, our model separates the visual style from digit type on MNIST, and separates shape and pose in images of the human body.

Connected through hyperedge HE-1 (induced by source paper "an explicit local and global representation disentanglement framework with applications in deep clustering and unsupervised object detection"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "implicit autoencoders"
    - "an explicitly relational neural network architecture"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning latent representations in neural networks for clustering through pseudo supervision and graph based activity regularization"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "lstm a search space odyssey"

Connected through hyperedge HE-2 (induced by source paper "convolutional stn for weakly supervised object localization and beyond"):
  with members:
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "adversarial complementary learning for weakly supervised object localization"
    - "active convolution learning the shape of convolution for image classification"
    - "dota a large scale dataset for object detection in aerial images"
    - "deformable convolutional networks"
    - "repulsion loss detecting pedestrians in a crowd"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "multiple instance detection network with online instance classifier refinement"

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)