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: "spectral inference networks unifying spectral methods with deep learning"
  Abstract: We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or pairs of data. We derive a training algorithm for Spectral Inference Networks that addresses the bias in the gradients due to finite batch size and allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets as well as the Arcade Learning Environment. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators, can discover interpretable representations from video and find meaningful subgoals in reinforcement learning environments.

Connected through hyperedge HE-1 (induced by source paper "data efficient image recognition with contrastive predictive coding"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "fast autoaugment"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "unsupervised data augmentation for consistency training"

Connected through hyperedge HE-2 (induced by source paper "measuring the data efficiency of deep learning methods"):
  with members:
    - "low shot learning from imaginary data"
    - "gradient based training of slow feature analysis by differentiable approximate whitening"
    - "data efficient reinforcement learning with probabilistic model predictive control"
    - "learning feed forward one shot learners"
    - "improved graph based sfa information preservation complements the slowness principle"
    - "adam a method for stochastic optimization"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "auto vectorizing tensorflow graphs jacobians auto batching and beyond"):
  with members:
    - "dynamic control flow in large scale machine learning"
    - "deep learning with dynamic computation graphs"
    - "dlvm a modern compiler infrastructure for deep learning systems"
    - "neural message passing for quantum chemistry"
    - "adaptive computation time for recurrent neural networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "gradient based training of slow feature analysis by differentiable approximate whitening"):
  with members:
    - "unsupervised learning of semantic audio representations"
    - "improved graph based sfa information preservation complements the slowness principle"
    - "xception deep learning with depthwise separable convolutions"
    - "unsupervised learning of visual representations using videos"
    - "unsupervised feature learning from temporal data"
    - "slow and steady feature analysis higher order temporal coherence in video"
    - "learning to linearize under uncertainty"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "the laplacian in rl learning representations with efficient approximations"):
  with members:
    - "temporal difference models model free deep rl for model based control"
    - "data efficient hierarchical reinforcement learning"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "eigenoption discovery through the deep successor representation"
    - "curiosity driven exploration by self supervised prediction"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "scale up nonlinear component analysis with 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)