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: "sublinear optimization for machine learning"
  Abstract: We give sublinear-time approximation algorithms for some optimization problems arising in machine learning, such as training linear classifiers and finding minimum enclosing balls. Our algorithms can be extended to some kernelized versions of these problems, such as SVDD, hard margin SVM, and L2-SVM, for which sublinear-time algorithms were not known before. These new algorithms use a combination of a novel sampling techniques and a new multiplicative update algorithm. We give lower bounds which show the running times of many of our algorithms to be nearly best possible in the unit-cost RAM model. We also give implementations of our algorithms in the semi-streaming setting, obtaining the first low pass polylogarithmic space and sublinear time algorithms achieving arbitrary approximation factor.

Connected through hyperedge HE-1 (induced by source paper "on coresets for support vector machines"):
  with members:
    - "provable filter pruning for efficient neural networks"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "new frameworks for offline and streaming coreset constructions"
    - "accurate streaming support vector machines"
    - "fast svm training using approximate extreme points"
    - "a unified framework for approximating and clustering data"
    - "streamed learning one pass svms"

Connected through hyperedge HE-2 (induced by source paper "stochastic optimization for regularized wasserstein estimators"):
  with members:
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "stochastic wasserstein barycenters"
    - "unsupervised hyperalignment for multilingual word embeddings"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "parallel streaming wasserstein barycenters"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

Connected through hyperedge HE-3 (induced by source paper "minimum enclosing ball revisited stability sub linear time algorithms and extension"):
  with members:
    - "beyond worst case analysis"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "distributed partial clustering"
    - "coresets and sketches"
    - "locating a small cluster privately"
    - "the non uniform k center problem"
    - "k center clustering under perturbation resilience"
    - "optimization algorithms for faster computational geometry"

Connected through hyperedge HE-4 (induced by source paper "minimum enclosing ball revisited stability and sub linear time algorithms"):
  with members:
    - "beyond worst case analysis"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "coresets and sketches"
    - "locating a small cluster privately"
    - "k center clustering under perturbation resilience"
    - "optimization algorithms for faster computational geometry"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "clustering under perturbation resilience"

Connected through hyperedge HE-5 (induced by source paper "introduction to online convex optimization"):
  with members:
    - "online linear optimization via smoothing"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "block coordinate frank wolfe optimization for structural svms"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "projection free online learning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "large scale convex minimization with a low rank constraint"

Connected through hyperedge HE-6 (induced by source paper "quantum and classical algorithms for approximate submodular function minimization"):
  with members:
    - "near optimal approximate discrete and continuous submodular function minimization"
    - "subquadratic submodular function minimization"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-7 (induced by source paper "online to offline conversions universality and adaptive minibatch sizes"):
  with members:
    - "the power of normalization faster evasion of saddle points"
    - "beyond convexity stochastic quasi convex optimization"
    - "distributed mini batch sdca"
    - "adam a method for stochastic optimization"
    - "adadelta an adaptive learning rate method"
    - "linear regression with limited observation"
    - "better mini batch algorithms via accelerated gradient methods"
    - "adaptive bound optimization for online convex optimization"

Connected through hyperedge HE-8 (induced by source paper "primal dual π learning sample complexity and sublinear run time for ergodic markov decision problems"):
  with members:
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"
    - "lower bound on the computational complexity of discounted markov decision problems"
    - "on the complexity of policy iteration"
    - "on the sample complexity of reinforcement learning with a generative model"
    - "pac bounds for discounted mdps"

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)