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: "online learning with gated linear networks"
  Abstract: This paper describes a family of probabilistic architectures designed for online learning under the logarithmic loss. Rather than relying on non-linear transfer functions, our method gains representational power by the use of data conditioning. We state under general conditions a learnable capacity theorem that shows this approach can in principle learn any bounded Borel-measurable function on a compact subset of euclidean space; the result is stronger than many universality results for connectionist architectures because we provide both the model and the learning procedure for which convergence is guaranteed.

Connected through hyperedge HE-1 (induced by source paper "product kanerva machines factorized bayesian memory"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "monet unsupervised scene decomposition and representation"
    - "generalization of reinforcement learners with working and episodic memory"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "metalearned neural memory"
    - "recurrent independent mechanisms"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "shaping belief states with generative environment models for rl"

Connected through hyperedge HE-2 (induced by source paper "dawson a domain adaptive few shot generation framework"):
  with members:
    - "metalgan multi domain label less image synthesis using cgans and meta learning"
    - "figr few shot image generation with reptile"
    - "counterpoint by convolution"
    - "this time with feeling learning expressive musical performance"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "sample efficient adaptive text to speech"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "variational memory addressing in generative models"

Connected through hyperedge HE-3 (induced by source paper "online learning in contextual bandits using gated linear networks"):
  with members:
    - "gated linear networks"
    - "parameter space noise for exploration"
    - "a distributional perspective on reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "unifying count based exploration and intrinsic motivation"
    - "compress and control"
    - "linear and geometric mixtures analysis"

Connected through hyperedge HE-4 (induced by source paper "low complexity nonparametric bayesian online prediction with universal guarantees"):
  with members:
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "context tree switching"

Connected through hyperedge HE-5 (induced by source paper "gated linear networks"):
  with members:
    - "pixel recurrent neural networks"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "linear and geometric mixtures analysis"
    - "a machine learning perspective on predictive coding with paq"

Connected through hyperedge HE-6 (induced by source paper "sample efficient adaptive text to speech"):
  with members:
    - "neural voice cloning with a few samples"
    - "playing hard exploration games by watching youtube"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "observe and look further achieving consistent performance on atari"
    - "fitting new speakers based on a short untranscribed sample"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "voiceloop voice fitting and synthesis via a phonological loop"

Connected through hyperedge HE-7 (induced by source paper "associative compression networks for representation learning"):
  with members:
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "vae with a vampprior"
    - "pixelsnail an improved autoregressive generative model"
    - "neural discrete representation learning"
    - "an architecture for deep hierarchical generative models"
    - "adversarial feature learning"
    - "pixelvae a latent variable model for natural images"
    - "matching networks for one shot learning"

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)