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: "momentum based variance reduction in non convex sgd"
  Abstract: Variance reduction has emerged in recent years as a strong competitor to stochastic gradient descent in non-convex problems, providing the first algorithms to improve upon the converge rate of stochastic gradient descent for finding first-order critical points. However, variance reduction techniques typically require carefully tuned learning rates and willingness to use excessively large "mega-batches" in order to achieve their improved results. We present a new algorithm, STORM, that does not require any batches and makes use of adaptive learning rates, enabling simpler implementation and less hyperparameter tuning. Our technique for removing the batches uses a variant of momentum to achieve variance reduction in non-convex optimization. On smooth losses $F$, STORM finds a point $\boldsymbol{x}$ with $\mathbb{E}[\|\nabla F(\boldsymbol{x})\|]\le O(1/\sqrt{T}+\sigma^{1/3}/T^{1/3})$ in $T$ iterations with $\sigma^2$ variance in the gradients, matching the optimal rate but without requiring knowledge of $\sigma$.

Connected through hyperedge HE-1 (induced by source paper "a hybrid stochastic policy gradient algorithm for reinforcement learning"):
  with members:
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "policy optimization with stochastic mirror descent"
    - "regularization matters in policy optimization"
    - "sample efficient policy gradient methods with recursive variance reduction"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "stochastic variance reduced policy gradient"
    - "exploring generalization in deep learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-2 (induced by source paper "momentum improves normalized sgd"):
  with members:
    - "reducing the variance in online optimization by transporting past gradients"
    - "memory efficient adaptive optimization"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "on the convergence of adam and beyond"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"

Connected through hyperedge HE-3 (induced by source paper "distributed momentum for byzantine resilient learning"):
  with members:
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "can you really backdoor federated learning"
    - "sgd decentralized byzantine resilience"
    - "a little is enough circumventing defenses for distributed learning"
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"

Connected through hyperedge HE-4 (induced by source paper "decaying momentum helps neural network training"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "on the convergence of adam and beyond"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "aggregated momentum stability through passive damping"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "negative momentum for improved game dynamics"
    - "regularizing deep networks using efficient layerwise adversarial training"

Connected through hyperedge HE-5 (induced by source paper "efficient projection free online methods with stochastic recursive gradient"):
  with members:
    - "introduction to online convex optimization"
    - "quantized frank wolfe faster optimization lower communication and projection free"
    - "online learning a comprehensive survey"
    - "variance reduced and projection free stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "block coordinate frank wolfe optimization for structural svms"
    - "projection free online learning"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-6 (induced by source paper "stochastic recursive gradient based methods for projection free online learning"):
  with members:
    - "introduction to online convex optimization"
    - "quantized frank wolfe faster optimization lower communication and projection free"
    - "online learning a comprehensive survey"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "block coordinate frank wolfe optimization for structural svms"
    - "projection free online learning"
    - "optimal distributed online prediction using mini batches"

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)