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: "learning koopman invariant subspaces for dynamic mode decomposition"
  Abstract: Spectral decomposition of the Koopman operator is attracting attention as a tool for the analysis of nonlinear dynamical systems. Dynamic mode decomposition is a popular numerical algorithm for Koopman spectral analysis; however, we often need to prepare nonlinear observables manually according to the underlying dynamics, which is not always possible since we may not have any a priori knowledge about them. In this paper, we propose a fully data-driven method for Koopman spectral analysis based on the principle of learning Koopman invariant subspaces from observed data. To this end, we propose minimization of the residual sum of squares of linear least-squares regression to estimate a set of functions that transforms data into a form in which the linear regression fits well. We introduce an implementation with neural networks and evaluate performance empirically using nonlinear dynamical systems and applications.

Connected through hyperedge HE-1 (induced by source paper "deep learning of dynamical attractors from time series measurements"):
  with members:
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "learning low dimensional state embeddings and metastable clusters from time series data"
    - "persistent homology of attractors for action recognition"
    - "reducing overfitting in deep networks by decorrelating representations"

Connected through hyperedge HE-2 (induced by source paper "learning compositional koopman operators for model based control"):
  with members:
    - "deep variational koopman models inferring koopman observations for uncertainty aware dynamics modeling and control"
    - "modeling and control of soft robots using the koopman operator and model predictive control"
    - "nonlinear system identification of soft robot dynamics using koopman operator theory"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "learning latent dynamics for planning from pixels"
    - "graph networks as learnable physics engines for inference and control"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"

Connected through hyperedge HE-3 (induced by source paper "deep variational koopman models inferring koopman observations for uncertainty aware dynamics modeling and control"):
  with members:
    - "learning latent dynamics for planning from pixels"
    - "visual reinforcement learning with imagined goals"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "embed to control a locally linear latent dynamics model for control from raw images"

Connected through hyperedge HE-4 (induced by source paper "physically interpretable classification of network dynamics for complex collective motions"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"

Connected through hyperedge HE-5 (induced by source paper "model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders"):
  with members:
    - "deep dynamical modeling and control of unsteady fluid flows"
    - "conservative model reduction for finite volume models"
    - "galerkin v least squares petrov galerkin projection in nonlinear model reduction"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "adaptive h refinement for reduced order models"

Connected through hyperedge HE-6 (induced by source paper "learning deep neural network representations for koopman operators of nonlinear dynamical systems"):
  with members:
    - "adam a method for stochastic optimization"
    - "better mixing via deep representations"
    - "the meaning of structure in interconnected dynamic systems"

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)