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 "differentiable visual computing"):
  Abstract: Derivatives of computer graphics, image processing, and deep learning algorithms have tremendous use in guiding parameter space searches, or solving inverse problems. As the algorithms become more sophisticated, we no longer only need to differentiate simple mathematical functions, but have to deal with general programs which encode complex transformations of data. This dissertation introduces three tools for addressing the challenges that arise when obtaining and applying the derivatives for complex graphics algorithms. #R##N#Traditionally, practitioners have been constrained to composing programs with a limited set of operators, or hand-deriving derivatives. We extend the image processing language Halide with reverse-mode automatic differentiation, and the ability to automatically optimize the gradient computations. This enables automatic generation of the gradients of arbitrary Halide programs, at high performance, with little programmer effort. #R##N#In 3D rendering, the gradient is required with respect to variables such as camera parameters, geometry, and appearance. However, computing the gradient is challenging because the rendering integral includes visibility terms that are not differentiable. We introduce, to our knowledge, the first general-purpose differentiable ray tracer that solves the full rendering equation, while correctly taking the geometric discontinuities into account. #R##N#Finally, we demonstrate that the derivatives of light path throughput can also be useful for guiding sampling in forward rendering. Simulating light transport in the presence of multi-bounce glossy effects and motion in 3D rendering is challenging due to the hard-to-sample high-contribution areas. We present a Markov Chain Monte Carlo rendering algorithm that extends Metropolis Light Transport by automatically and explicitly adapting to the local integrand, thereby increasing sampling efficiency.

Includes members:
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "elimination of all bad local minima in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "the lottery ticket hypothesis finding sparse trainable neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lossless compression of deep neural networks")
  Member "stochastic gradient descent with biased but consistent gradient estimators":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sample complexity of estimating the policy gradient for nearly deterministic dynamical systems")
  Member "line drawings from 3d models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "im2pencil controllable pencil illustration from photographs")
  Member "tiramisu a polyhedral compiler for expressing fast and portable code":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "polyscientist automatic loop transformations combined with microkernels for optimization of deep learning primitives")
  Member "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "seqxy2seqz structure learning for 3d shapes by sequentially predicting 1d occupancy segments from 2d coordinates")
  Member "tangent automatic differentiation using source code transformation in python":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relay a high level ir for deep learning")

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.GR(Graphics)