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: "doubly stochastic adversarial autoencoder"
  Abstract: Any autoencoder network can be turned into a generative model by imposing an arbitrary prior distribution on its hidden code vector. Variational Autoencoder uses a KL divergence penalty to impose the prior, whereas Adversarial Autoencoder uses generative adversarial networks. A straightforward modification of Adversarial Autoencoder can be achieved by replacing the adversarial network with maximum mean discrepancy (MMD) network. This replacement leads to a new set of probabilistic autoencoder which is also discussed in our paper. However, an essential challenge remains in both of these probabilistic autoencoders, namely that the only source of randomness at the output of encoder, is the training data itself. Lack of enough stochasticity can make the optimization problem non-trivial. As a result, they can lead to degenerate solutions where the generator collapses into sampling only a few modes. Our proposal is to replace the adversary of the adversarial autoencoder by a space of {\it stochastic} functions. This replacement introduces a a new source of randomness which can be considered as a continuous control for encouraging {\it explorations}. This prevents the adversary from fitting too closely to the generator and therefore leads to more diverse set of generated samples. Consequently, the decoder serves as a better generative network which unlike MMD nets scales linearly with the amount of data. We provide mathematical and empirical evidence on how this replacement outperforms the pre-existing architectures.

Connected through hyperedge HE-1 (induced by source paper "fuzzy hashing as perturbation consistent adversarial kernel embedding"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "scalable kernel methods via doubly stochastic gradients"

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.LG(Machine Learning)