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 distributed semi stochastic gradient optimization"
  Abstract: With the recent proliferation of large-scale learning problems,there have been a lot of interest on distributed machine learning algorithms, particularly those that are based on stochastic gradient descent (SGD) and its variants. However, existing algorithms either suffer from slow convergence due to the inherent variance of stochastic gradients, or have a fast linear convergence rate but at the expense of poorer solution quality. In this paper, we combine their merits by proposing a fast distributed asynchronous SGD-based algorithm with variance reduction. A constant learning rate can be used, and it is also guaranteed to converge linearly to the optimal solution. Experiments on the Google Cloud Computing Platform demonstrate that the proposed algorithm outperforms state-of-the-art distributed asynchronous algorithms in terms of both wall clock time and solution quality.

Connected through hyperedge HE-1 (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"
    - "momentum based variance reduction in non convex sgd"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "gradient scheduling with global momentum for non iid data distributed asynchronous training"):
  with members:
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "federated learning with non iid data"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "hello edge keyword spotting on microcontrollers"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "asynchronous stochastic gradient descent with delay compensation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "personalized speech recognition on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "async a cloud engine with asynchrony and history for distributed machine learning"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "speeding up distributed machine learning using codes"
    - "probabilistic synchronous parallel"
    - "revisiting distributed synchronous sgd"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "asynchronous distributed admm for large scale optimization part i algorithm and convergence analysis"
    - "web scale topic models in spark an asynchronous parameter server"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"

Connected through hyperedge HE-4 (induced by source paper "distributed asynchronous dual free stochastic dual coordinate ascent"):
  with members:
    - "sdca without duality regularization and individual convexity"
    - "asynchronous stochastic gradient descent with variance reduction for non convex optimization"
    - "adding vs averaging in distributed primal dual optimization"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "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-5 (induced by source paper "asynchronous stochastic gradient descent with variance reduction for non convex optimization"):
  with members:
    - "distributed asynchronous dual free stochastic dual coordinate ascent"
    - "decoupled asynchronous proximal stochastic gradient descent with variance reduction"
    - "asynchronous stochastic block coordinate descent with variance reduction"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "a distributed asynchronous and incremental algorithm for nonconvex optimization an admm based approach"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-6 (induced by source paper "decoupled asynchronous proximal stochastic gradient descent with variance reduction"):
  with members:
    - "asynchronous stochastic gradient descent with variance reduction for non convex optimization"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "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)