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: "asynchronous stochastic gradient descent with variance reduction for non convex optimization"
  Abstract: We provide the first theoretical analysis on the convergence rate of the asynchronous stochastic variance reduced gradient (SVRG) descent algorithm on non-convex optimization. Recent studies have shown that the asynchronous stochastic gradient descent (SGD) based algorithms with variance reduction converge with a linear convergent rate on convex problems. However, there is no work to analyze asynchronous SGD with variance reduction technique on non-convex problem. In this paper, we study two asynchronous parallel implementations of SVRG: one is on a distributed memory system and the other is on a shared memory system. We provide the theoretical analysis that both algorithms can obtain a convergence rate of $O(1/T)$, and linear speed up is achievable if the number of workers is upper bounded. V1,v2,v3 have been withdrawn due to reference issue, please refer the newest version v4.

Connected through hyperedge HE-1 (induced by source paper "adaptive weight decay for deep neural networks"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "don t decay the learning rate increase the batch size"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "sharp minima can generalize for deep nets"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "straggler agnostic and communication efficient distributed primal dual algorithm for high dimensional data mining"):
  with members:
    - "diversely stale parameters for efficient training of cnns"
    - "ouroboros on accelerating training of transformer based language models"
    - "training neural networks using features replay"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "sparse communication for distributed gradient descent"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "adding vs averaging in distributed primal dual optimization"

Connected through hyperedge HE-3 (induced by source paper "stochastic doubly robust gradient"):
  with members:
    - "not all samples are created equal deep learning with importance sampling"
    - "more robust doubly robust off policy evaluation"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "importance sampling with unequal support"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "new optimisation methods for machine learning"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-4 (induced by source paper "taming convergence for asynchronous stochastic gradient descent with unbounded delay in non convex learning"):
  with members:
    - "parallel matrix factorization for low rank tensor completion"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"
    - "parallel training of dnns with natural gradient and parameter averaging"

Connected through hyperedge HE-5 (induced by source paper "an asynchronous parallel approach to sparse recovery"):
  with members:
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "revisiting asynchronous linear solvers provable convergence rate through randomization"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "distributed asynchronous dual free stochastic dual coordinate ascent"):
  with members:
    - "sdca without duality regularization and individual convexity"
    - "adding vs averaging in distributed primal dual optimization"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "asynchronous distributed semi stochastic gradient optimization"
    - "sdca without duality"
    - "distributed mini batch sdca"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "staleness aware async sgd for distributed deep learning"

Connected through hyperedge HE-7 (induced by source paper "asynchronous stochastic block coordinate descent with variance reduction"):
  with members:
    - "asynchronous stochastic proximal optimization algorithms with variance reduction"
    - "distributed asynchronous dual free stochastic dual coordinate ascent"
    - "sdca without duality regularization and individual convexity"
    - "decoupled asynchronous proximal stochastic gradient descent with variance reduction"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "revisiting asynchronous linear solvers provable convergence rate through randomization"
    - "parallel coordinate descent for l1 regularized loss minimization"

Connected through hyperedge HE-8 (induced by source paper "decoupled asynchronous proximal stochastic gradient descent with variance reduction"):
  with members:
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "asynchronous distributed semi stochastic gradient optimization"
    - "distributed mini batch sdca"
    - "staleness aware async sgd for distributed deep learning"

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)