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: "stochastic variance reduction methods for saddle point problems"
  Abstract: We consider convex-concave saddle-point problems where the objective functions may be split in many components, and extend recent stochastic variance reduction methods (such as SVRG or SAGA) to provide the first large-scale linearly convergent algorithms for this class of problems which is common in machine learning. While the algorithmic extension is straightforward, it comes with challenges and opportunities: (a) the convex minimization analysis does not apply and we use the notion of monotone operators to prove convergence, showing in particular that the same algorithm applies to a larger class of problems, such as variational inequalities, (b) there are two notions of splits, in terms of functions, or in terms of partial derivatives, (c) the split does need to be done with convex-concave terms, (d) non-uniform sampling is key to an efficient algorithm, both in theory and practice, and (e) these incremental algorithms can be easily accelerated using a simple extension of the "catalyst" framework, leading to an algorithm which is always superior to accelerated batch algorithms.

Connected through hyperedge HE-1 (induced by source paper "accelerating smooth games by manipulating spectral shapes"):
  with members:
    - "a tight and unified analysis of extragradient for a whole spectrum of differentiable games"
    - "differentiable game mechanics"
    - "a variational inequality perspective on generative adversarial nets"
    - "the mechanics of n player differentiable games"
    - "the numerics of gans"
    - "training gans with optimism"

Connected through hyperedge HE-2 (induced by source paper "stochastic recursive gradient descent ascent for stochastic nonconvex strongly concave minimax problems"):
  with members:
    - "a stochastic proximal point algorithm for saddle point problems"
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "stochastic variance reduction methods for policy evaluation"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-3 (induced by source paper "stochastic proximal auc maximization"):
  with members:
    - "introduction to online convex optimization"
    - "support vector algorithms for optimizing the partial area under the roc curve"
    - "simultaneous model selection and optimization through parameter free stochastic learning"
    - "one pass auc optimization"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "stochastic optimization for machine learning"
    - "on the consistency of auc pairwise optimization"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving off policy evaluation"):
  with members:
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "renyi differential privacy"
    - "stochastic variance reduction methods for policy evaluation"
    - "deep reinforcement learning for sepsis treatment"
    - "concentrated differential privacy"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-5 (induced by source paper "svrg for policy evaluation with fewer gradient evaluations"):
  with members:
    - "stochastic variance reduced policy gradient"
    - "stochastic variance reduction methods for policy evaluation"
    - "convergent tree backup and retrace with function approximation"
    - "stop wasting my gradients practical svrg"
    - "on td 0 with function approximation concentration bounds and a centered variant with exponential convergence"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-6 (induced by source paper "principal component projection and regression in nearly linear time through asymmetric svrg"):
  with members:
    - "stability of the lanczos method for matrix function approximation"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "principal component projection without principal component analysis"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-7 (induced by source paper "exponential stability of primal dual gradient dynamics with non strong convexity"):
  with members:
    - "stochastic variance reduction methods for policy evaluation"
    - "design and stability of load side primary frequency control in power systems"
    - "convergence analysis of saddle point problems in time varying wireless systems control theoretical approach"

Connected through hyperedge HE-8 (induced by source paper "stochastic primal dual algorithms with faster convergence than o 1 t for problems without bilinear structure"):
  with members:
    - "robust optimization for non convex objectives"

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)