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: "exploiting the structure stochastic gradient methods using raw clusters"
  Abstract: The amount of data available in the world is growing faster and bigger than our ability to deal with it. However, if we take advantage of the internal structure, data may become much smaller for machine learning purposes. In this paper we focus on one of the most fundamental machine learning tasks, empirical risk minimization (ERM), and provide faster algorithms with the help from the clustering structure of the data. #R##N#We introduce a simple notion of raw clustering that can be efficiently obtained with just one pass of the data, and propose two algorithms. Our variance-reduction based algorithm ClusterSVRG introduces a new gradient estimator using the clustering information, and our accelerated algorithm ClusterACDM is built on a novel Haar transformation applied to the dual space of each cluster. Our algorithms outperform their classical counterparts both in theory and practice.

Connected through hyperedge HE-1 (induced by source paper "data sketching for faster training of machine learning models"):
  with members:
    - "not all samples are created equal deep learning with importance sampling"
    - "variance reduced stochastic gradient descent with neighbors"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "multi resolution hashing for fast pairwise summations"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "hashing based estimators for kernel density in high dimensions"
    - "on the fine grained complexity of empirical risk minimization kernel methods and neural networks"
    - "stochastic optimization with bandit sampling"
    - "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search"
    - "tight cell probe bounds for succinct boolean matrix vector multiplication"
    - "distance sensitive hashing"

Connected through hyperedge HE-3 (induced by source paper "stochastic distributed and federated optimization for machine learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "randomized distributed mean estimation accuracy vs communication"
    - "sdca without duality regularization and individual convexity"
    - "importance sampling for minibatches"
    - "practical secure aggregation for federated learning on user held data"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-4 (induced by source paper "lower bounds on time space trade offs for approximate near neighbors"):
  with members:
    - "randomized approximate nearest neighbor search with limited adaptivity"
    - "simple average case lower bounds for approximate near neighbor from isoperimetric inequalities"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "scalability and total recall with fast coveringlsh"
    - "learning to hash for indexing big data a survey"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "practical and optimal lsh for angular distance"

Connected through hyperedge HE-5 (induced by source paper "federated optimization distributed machine learning for on device intelligence"):
  with members:
    - "sdca without duality regularization and individual convexity"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed optimization beyond the datacenter"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "stop wasting my gradients practical svrg"
    - "distributed optimization with arbitrary local solvers"
    - "distributed mini batch sdca"
    - "l1 regularized distributed optimization a communication efficient primal dual framework"

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)