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: "approximate program smoothing using mean variance statistics with application to procedural shader bandlimiting"
  Abstract: This paper introduces a general method to approximate the convolution of an arbitrary program with a Gaussian kernel. This process has the effect of smoothing out a program. Our compiler framework models intermediate values in the program as random variables, by using mean and variance statistics. Our approach breaks the input program into parts and relates the statistics of the different parts, under the smoothing process. We give several approximations that can be used for the different parts of the program. These include the approximation of Dorn et al., a novel adaptive Gaussian approximation, Monte Carlo sampling, and compactly supported kernels. Our adaptive Gaussian approximation is accurate up to the second order in the standard deviation of the smoothing kernel, and mathematically smooth. We show how to construct a compiler that applies chosen approximations to given parts of the input program. Because each expression can have multiple approximation choices, we use a genetic search to automatically select the best approximations. We apply this framework to the problem of automatically bandlimiting procedural shader programs. We evaluate our method on a variety of complex shaders, including shaders with parallax mapping, animation, and spatially varying statistics. The resulting smoothed shader programs outperform previous approaches both numerically, and aesthetically, due to the smoothing properties of our approximations.

Connected through hyperedge HE-1 (induced by source paper "differentiable visual computing"):
  with members:
    - "on the convergence of adam and beyond"
    - "elimination of all bad local minima in deep learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "line drawings from 3d models"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "tangent automatic differentiation using source code transformation in python"

Connected through hyperedge HE-2 (induced by source paper "algonet c infty smooth algorithmic neural networks"):
  with members:
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "inverse transport networks"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "explaining explanations an overview of interpretability of machine learning"
    - "adam a method for stochastic optimization"

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.GR(Graphics)