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: "scale free algorithms for online linear optimization"
  Abstract: We design algorithms for online linear optimization that have optimal regret and at the same time do not need to know any upper or lower bounds on the norm of the loss vectors. We achieve adaptiveness to norms of loss vectors by scale invariance, i.e., our algorithms make exactly the same decisions if the sequence of loss vectors is multiplied by any positive constant. Our algorithms work for any decision set, bounded or unbounded. For unbounded decisions sets, these are the first truly adaptive algorithms for online linear optimization.

Connected through hyperedge HE-1 (induced by source paper "training neural networks for and by interpolation"):
  with members:
    - "sgd converges to global minimum in deep learning via star convex path"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "deepobs a deep learning optimizer benchmark suite"
    - "on the convergence of adam and beyond"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "deep frank wolfe for neural network optimization"

Connected through hyperedge HE-2 (induced by source paper "global convergence of adaptive gradient methods for an over parameterized neural network"):
  with members:
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "a sufficient condition for convergences of adam and rmsprop"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "signsgd compressed optimisation for non convex problems"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-3 (induced by source paper "adaptive scale invariant online algorithms for learning linear models"):
  with members:
    - "introduction to online convex optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "training deep networks without learning rates through coin betting"
    - "online learning without prior information"
    - "coin betting and parameter free online learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "simultaneous model selection and optimization through parameter free stochastic learning"

Connected through hyperedge HE-4 (induced by source paper "exploration enhanced politex"):
  with members:
    - "provably efficient maximum entropy exploration"
    - "deepmind control suite"
    - "large scale study of curiosity driven learning"
    - "model free linear quadratic control via reduction to expert prediction"
    - "distributed prioritized experience replay"
    - "is q learning provably efficient"
    - "multi step reinforcement learning a unifying algorithm"
    - "prioritized experience replay"

Connected through hyperedge HE-5 (induced by source paper "online adaptive methods universality and acceleration"):
  with members:
    - "black box reductions for parameter free online learning in banach spaces"
    - "adaptive bound optimization for online convex optimization"

Connected through hyperedge HE-6 (induced by source paper "training deep networks without learning rates through coin betting"):
  with members:
    - "online convex optimization with unconstrained domains and losses"
    - "online learning without prior information"
    - "coin betting and parameter free online learning"
    - "gradient descent learns linear dynamical systems"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"
    - "simultaneous model selection and optimization through parameter free stochastic learning"
    - "unconstrained online linear learning in hilbert spaces minimax algorithms and normal approximations"

Connected through hyperedge HE-7 (induced by source paper "scale invariant unconstrained online learning"):
  with members:
    - "introduction to online convex optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "online convex optimization with unconstrained domains and losses"
    - "training deep networks without learning rates through coin betting"
    - "online learning without prior information"
    - "efficient second order online learning by sketching"
    - "coin betting and parameter free online learning"
    - "simultaneous model selection and optimization through parameter free stochastic learning"

Connected through hyperedge HE-8 (induced by source paper "efficient second order online learning by sketching"):
  with members:
    - "solving ridge regression using sketched preconditioned svrg"
    - "efficient frequent directions algorithm for sparse matrices"
    - "the fast convergence of incremental pca"
    - "frequent directions simple and deterministic matrix sketching"
    - "faster sgd using sketched conditioning"
    - "sketching as a tool for numerical linear algebra"
    - "a linearly convergent conditional gradient algorithm with applications to online and stochastic optimization"
    - "one pass auc optimization"

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)