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: "linear coupling an ultimate unification of gradient and mirror descent"
  Abstract: First-order methods play a central role in large-scale convex optimization. Even though many variations exist, each suited to a particular problem form, almost all such methods fundamentally rely on two types of algorithmic steps and two corresponding types of analysis: gradient-descent steps, which yield primal progress, and mirror-descent steps, which yield dual progress. In this paper, we observe that the performances of these two types of step are complementary, so that faster algorithms can be designed by linearly coupling the two steps. #R##N#In particular, we obtain a simple accelerated gradient method for the class of smooth convex optimization problems. The first such method was proposed by Nesterov back to 1983, but to the best of our knowledge, the proof of the fast convergence of accelerated gradient methods has not found a clear interpretation and is still regarded by many as crucially relying on "algebraic tricks". We apply our novel insights to construct a new accelerated gradient method as a natural linear coupling of gradient descent and mirror descent and to write its proof of convergence as a simple combination of the convergence analyses of the two underlying descent steps. #R##N#We believe that the complementary view and the linear coupling technique in this paper will prove very useful in the design of first-order methods as it allows us to design fast algorithms in a conceptually easier way. For instance, our technique greatly facilitates the recent breakthroughs in solving packing and covering linear programs [AO14, AO15].

Connected through hyperedge HE-1 (induced by source paper "faster on device training using new federated momentum algorithm"):
  with members:
    - "advances and open problems in federated learning"
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"

Connected through hyperedge HE-2 (induced by source paper "on the convergence of nesterov s accelerated gradient method in stochastic settings"):
  with members:
    - "accelerating sgd with momentum for over parameterized learning"
    - "sgd general analysis and improved rates"
    - "understanding the role of momentum in stochastic gradient methods"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "negative momentum for improved game dynamics"
    - "on the curved geometry of accelerated optimization"

Connected through hyperedge HE-3 (induced by source paper "accelerating gradient boosting machine"):
  with members:
    - "proximal boosting and its acceleration"
    - "global linear convergence of newton s method without strong convexity or lipschitz gradients"
    - "randomized gradient boosting machine"
    - "accelerated proximal boosting"
    - "xgboost a scalable tree boosting system"
    - "scikit learn machine learning in python"
    - "a primal dual convergence analysis of boosting"

Connected through hyperedge HE-4 (induced by source paper "social welfare and profit maximization from revealed preferences"):
  with members:
    - "introduction to online convex optimization"
    - "strategic classification from revealed preferences"
    - "multidimensional dynamic pricing for welfare maximization"
    - "watch and learn optimizing from revealed preferences feedback"
    - "learning economic parameters from revealed preferences"
    - "fast algorithms for online stochastic convex programming"
    - "online learning and profit maximization from revealed preferences"
    - "learning what s going on reconstructing preferences and priorities from opaque transactions"

Connected through hyperedge HE-5 (induced by source paper "much faster algorithms for matrix scaling"):
  with members:
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "using optimization to obtain a width independent parallel simpler and faster positive sdp solver"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel"
    - "an efficient parallel solver for sdd linear systems"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-6 (induced by source paper "accelerated gradient descent escapes saddle points faster than gradient descent"):
  with members:
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-7 (induced by source paper "potential function proofs for first order methods"):
  with members:
    - "introduction to online convex optimization"

Connected through hyperedge HE-8 (induced by source paper "faster parallel solver for positive linear programs via dynamically bucketed selective coordinate descent"):
  with members:
    - "using optimization to obtain a width independent parallel simpler and faster positive sdp solver"
    - "unified acceleration method for packing and covering problems via diameter reduction"
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"
    - "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel"
    - "nearly linear time approximation schemes for mixed packing covering and facility location linear programs"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
    - "parallel coordinate descent for l1 regularized loss minimization"
    - "sequential and parallel algorithms for mixed packing and covering"

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.DS(Data Structures and Algorithms)