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: "sdna stochastic dual newton ascent for empirical risk minimization"
  Abstract: We propose a new algorithm for minimizing regularized empirical loss: Stochastic Dual Newton Ascent (SDNA). Our method is dual in nature: in each iteration we update a random subset of the dual variables. However, unlike existing methods such as stochastic dual coordinate ascent, SDNA is capable of utilizing all curvature information contained in the examples, which leads to striking improvements in both theory and practice - sometimes by orders of magnitude. In the special case when an L2-regularizer is used in the primal, the dual problem is a concave quadratic maximization problem plus a separable term. In this regime, SDNA in each step solves a proximal subproblem involving a random principal submatrix of the Hessian of the quadratic function; whence the name of the method. If, in addition, the loss functions are quadratic, our method can be interpreted as a novel variant of the recently introduced Iterative Hessian Sketch.

Connected through hyperedge HE-1 (induced by source paper "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "direct acceleration of saga using sampled negative momentum"
    - "sdca without duality regularization and individual convexity"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "variance reduced stochastic gradient descent with neighbors"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "mini batch primal and dual methods for svms"

Connected through hyperedge HE-2 (induced by source paper "hemingway modeling distributed optimization algorithms"):
  with members:
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "learning to learn by gradient descent by gradient descent"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"
    - "learning to compose neural networks for question answering"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "designing neural network architectures using reinforcement learning"
    - "adding vs averaging in distributed primal dual optimization"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-3 (induced by source paper "a new perspective on randomized gossip algorithms"):
  with members:
    - "gossip algorithms for distributed signal processing"
    - "geographic gossip efficient averaging for sensor networks"
    - "order optimal consensus through randomized path averaging"

Connected through hyperedge HE-4 (induced by source paper "distributed optimization with arbitrary local solvers"):
  with members:
    - "importance sampling for minibatches"
    - "primal dual rates and certificates"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "distributed inexact damped newton method data partitioning and load balancing"
    - "adding vs averaging in distributed primal dual optimization"
    - "federated optimization distributed optimization beyond the datacenter"
    - "linear convergence of the randomized feasible descent method under the weak strong convexity assumption"
    - "sdca without duality"

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)