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: "tricks from deep learning"
  Abstract: The deep learning community has devised a diverse set of methods to make gradient optimization, using large datasets, of large and highly complex models with deeply cascaded nonlinearities, practical. Taken as a whole, these methods constitute a breakthrough, allowing computational structures which are quite wide, very deep, and with an enormous number and variety of free parameters to be effectively optimized. The result now dominates much of practical machine learning, with applications in machine translation, computer vision, and speech recognition. Many of these methods, viewed through the lens of algorithmic differentiation (AD), can be seen as either addressing issues with the gradient itself, or finding ways of achieving increased efficiency using tricks that are AD-related, but not provided by current AD systems.#R##N#The goal of this paper is to explain not just those methods of most relevance to AD, but also the technical constraints and mindset which led to their discovery. After explaining this context, we present a "laundry list" of methods developed by the deep learning community. Two of these are discussed in further mathematical detail: a way to dramatically reduce the size of the tape when performing reverse-mode AD on a (theoretically) time-reversible process like an ODE integrator; and a new mathematical insight that allows for the implementation of a stochastic Newton's method.

Connected through hyperedge HE-1 (induced by source paper "a simple differentiable programming language"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "efficient differentiable programming in a functional array processing language"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "dynamic control flow in large scale machine learning"
    - "the simple essence of automatic differentiation"
    - "tangent automatic differentiation using source code transformation for dynamically typed array programming"
    - "developing bug free machine learning systems with formal mathematics"
    - "a convenient differential category"

Connected through hyperedge HE-2 (induced by source paper "dynamic control flow in large scale machine learning"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep learning with dynamic computation graphs"
    - "real time machine learning the missing pieces"
    - "adaptive computation time for recurrent neural networks"
    - "globally normalized transition based neural networks"
    - "training deep nets with sublinear memory cost"
    - "memory efficient backpropagation through time"

Connected through hyperedge HE-3 (induced by source paper "automatic differentiation in machine learning a survey"):
  with members:
    - "confusion of tagged perturbations in forward automatic differentiation of higher order functions"
    - "divide and conquer checkpointing for arbitrary programs with no user annotation"
    - "tangent automatic differentiation using source code transformation in python"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "online learning rate adaptation with hypergradient descent"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "efficient implementation of a higher order language with built in ad"

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)