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 "backpack packing more into backprop"):
  Abstract: Automatic differentiation frameworks are optimized for exactly one thing: computing the average mini-batch gradient. Yet, other quantities such as the variance of the mini-batch gradients or many approximations to the Hessian can, in theory, be computed efficiently, and at the same time as the gradient. While these quantities are of great interest to researchers and practitioners, current deep-learning software does not support their automatic calculation. Manually implementing them is burdensome, inefficient if done naively, and the resulting code is rarely shared. This hampers progress in deep learning, and unnecessarily narrows research to focus on gradient descent and its variants; it also complicates replication studies and comparisons between newly developed methods that require those quantities, to the point of impossibility. To address this problem, we introduce BackPACK, an efficient framework built on top of PyTorch, that extends the backpropagation algorithm to extract additional information from first- and second-order derivatives. Its capabilities are illustrated by benchmark reports for computing additional quantities on deep neural networks, and an example application by testing several recent curvature approximations for optimization.

Includes members:
  Member "a modular approach to block diagonal hessian approximations for second order optimization methods":
    also connected through other hyperedges:
      (none)
  Member "deepobs a deep learning optimizer benchmark suite":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "limitations of the empirical fisher approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable and practical natural gradient for large scale deep learning")
  Member "don t unroll adjoint differentiating ssa form programs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentiate everything with a reversible programming language")
  Member "fast approximate natural gradient descent in a kronecker factored eigenbasis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differential dynamic programming neural optimizer")
  Member "not all samples are created equal deep learning with importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "no regret sample selection with noisy labels")
  Member "dissecting adam the sign magnitude and variance of stochastic gradients":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "laprop a better way to combine momentum with adaptive gradient")
  Member "coupling adaptive batch sizes with learning rates":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive regularization via residual smoothing in deep learning optimization")

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)