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 to importance sample in primary sample space"
  Abstract: Importance sampling is one of the most widely used variance reduction strategies in Monte Carlo rendering. In this paper, we propose a novel importance sampling technique that uses a neural network to learn how to sample from a desired density represented by a set of samples. Our approach considers an existing Monte Carlo rendering algorithm as a black box. During a scene-dependent training phase, we learn to generate samples with a desired density in the primary sample space of the rendering algorithm using maximum likelihood estimation. We leverage a recent neural network architecture that was designed to represent real-valued non-volume preserving ('Real NVP') transformations in high dimensional spaces. We use Real NVP to non-linearly warp primary sample space and obtain desired densities. In addition, Real NVP efficiently computes the determinant of the Jacobian of the warp, which is required to implement the change of integration variables implied by the warp. A main advantage of our approach is that it is agnostic of underlying light transport effects, and can be combined with many existing rendering techniques by treating them as a black box. We show that our approach leads to effective variance reduction in several practical scenarios.

Connected through hyperedge HE-1 (induced by source paper "neural importance sampling"):
  with members:
    - "neural autoregressive flows"
    - "neural ordinary differential equations"
    - "progressive growing of gans for improved quality stability and variation"
    - "notes on optimal approximations for importance sampling"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "learning light transport the reinforced way"
    - "integral equations and machine learning"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-2 (induced by source paper "deep learning the latent space of light transport"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "end to end sampling patterns"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "deep scattering rendering atmospheric clouds with radiance predicting neural networks"
    - "deep shading convolutional neural networks for screen space shading"
    - "learning a multi view stereo machine"
    - "learning light transport the reinforced way"
    - "pointnet deep learning on point sets for 3d classification and segmentation"

Connected through hyperedge HE-3 (induced by source paper "integral equations and machine learning"):
  with members:
    - "noise2noise learning image restoration without clean data"
    - "rainbow combining improvements in deep reinforcement learning"
    - "learning light transport the reinforced way"
    - "density estimation using real nvp"
    - "nice non linear independent components estimation"

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)