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: "associative adversarial networks"
  Abstract: We propose a higher-level associative memory for learning adversarial networks. Generative adversarial network (GAN) framework has a discriminator and a generator network. The generator (G) maps white noise (z) to data samples while the discriminator (D) maps data samples to a single scalar. To do so, G learns how to map from high-level representation space to data space, and D learns to do the opposite. We argue that higher-level representation spaces need not necessarily follow a uniform probability distribution. In this work, we use Restricted Boltzmann Machines (RBMs) as a higher-level associative memory and learn the probability distribution for the high-level features generated by D. The associative memory samples its underlying probability distribution and G learns how to map these samples to data space. The proposed associative adversarial networks (AANs) are generative models in the higher-levels of the learning, and use adversarial non-stochastic models D and G for learning the mapping between data and higher-level representation spaces. Experiments show the potential of the proposed networks.

Connected through hyperedge HE-1 (induced by source paper "memory augmented deep generative models for forecasting the next shot location in tennis"):
  with members:
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "deep decision trees for discriminative dictionary learning with adversarial multi agent trajectories"
    - "learning temporal strategic relationships using generative adversarial imitation learning"
    - "neural map structured memory for deep reinforcement learning"
    - "tree memory networks for modelling long term temporal dependencies"
    - "superposed episodic and semantic memory via sparse distributed representation"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "semi supervised learning with context conditional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "tree memory networks for modelling long term temporal dependencies"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "a deep multi level network for saliency prediction"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "pixel level domain transfer"

Connected through hyperedge HE-3 (induced by source paper "generative adversarial network based autoencoder application to fault detection problem for closed loop dynamical systems"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "parametrizing filters of a cnn with a gan"
    - "on convergence and stability of gans"
    - "one class adversarial nets for fraud detection"
    - "it takes only two adversarial generator encoder networks"
    - "ape gan adversarial perturbation elimination with gan"
    - "a classification based study of covariate shift in gan distributions"
    - "triple generative adversarial nets"

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)