human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "spectral inference networks unifying deep and spectral 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 graph-structured data. We cast training Spectral Inference Networks as a bilevel optimization problem, which 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. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner.

Includes members:
  Member "eigenoption discovery through the deep successor representation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "option discovery in the absence of rewards with manifold analysis")
  Member "a converse to banach s fixed point theorem and its cls completeness":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ppp completeness with connections to cryptography")
  Member "a laplacian framework for option discovery in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "options of interest temporal abstraction with interest functions")
  Member "connecting generative adversarial networks and actor critic methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "feature map level online adversarial knowledge distillation")
  Member "successor features for transfer in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "the arcade learning environment an evaluation platform for general agents":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "spectral networks and locally connected networks on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "closing the learning planning loop with predictive state representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised visuomotor control through distributional planning networks")

Question: Please tell me which class the source paper 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)