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: "primal dual rates and certificates"
  Abstract: We propose an algorithm-independent framework to equip existing optimization methods with primal-dual certificates. Such certificates and corresponding rate of convergence guarantees are important for practitioners to diagnose progress, in particular in machine learning applications. We obtain new primal-dual convergence rates, e.g., for the Lasso as well as many L1, Elastic Net, group Lasso and TV-regularized problems. The theory applies to any norm-regularized generalized linear model. Our approach provides efficiently computable duality gaps which are globally defined, without modifying the original problems in the region of interest.

Connected through hyperedge HE-1 (induced by source paper "on linear learning with manycore processors"):
  with members:
    - "parallel approximation of the maximum likelihood estimation for the prediction of large scale geostatistics simulations"
    - "partitioning compute units in cnn acceleration for statistical memory traffic shaping"
    - "faster coordinate descent via adaptive importance sampling"
    - "scaling deep learning on gpu and knights landing clusters"
    - "efficient use of limited memory accelerators for linear learning on heterogeneous systems"
    - "safe adaptive importance sampling"
    - "tensorflow a system for large scale machine learning"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"

Connected through hyperedge HE-2 (induced by source paper "cola decentralized linear learning"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "federated learning strategies for improving communication efficiency"
    - "communication compression for decentralized training"
    - "sparsified sgd with memory"
    - "d 2 decentralized training over decentralized data"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"
    - "federated multi task learning"
    - "cocoa a general framework for communication efficient distributed optimization"

Connected through hyperedge HE-3 (induced by source paper "distributed optimization with arbitrary local solvers"):
  with members:
    - "importance sampling for minibatches"
    - "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"
    - "sdna stochastic dual newton ascent for empirical risk minimization"
    - "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)