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: "distributed mini batch sdca"
  Abstract: We present an improved analysis of mini-batched stochastic dual coordinate ascent for regularized empirical loss minimization (i.e. SVM and SVM-type objectives). Our analysis allows for flexible sampling schemes, including where data is distribute across machines, and combines a dependence on the smoothness of the loss and/or the data spread (measured through the spectral norm).

Connected through hyperedge HE-1 (induced by source paper "stochastic distributed and federated optimization for machine learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "randomized distributed mean estimation accuracy vs communication"
    - "sdca without duality regularization and individual convexity"
    - "importance sampling for minibatches"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "practical secure aggregation for federated learning on user held data"

Connected through hyperedge HE-2 (induced by source paper "online to offline conversions universality and adaptive minibatch sizes"):
  with members:
    - "the power of normalization faster evasion of saddle points"
    - "beyond convexity stochastic quasi convex optimization"
    - "adam a method for stochastic optimization"
    - "adadelta an adaptive learning rate method"
    - "linear regression with limited observation"
    - "better mini batch algorithms via accelerated gradient methods"
    - "adaptive bound optimization for online convex optimization"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-3 (induced by source paper "large scale stochastic learning using gpus"):
  with members:
    - "understanding and optimizing the performance of distributed machine learning applications on apache spark"
    - "revisiting distributed synchronous sgd"
    - "adding vs averaging in distributed primal dual optimization"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "l1 regularized distributed optimization a communication efficient primal dual framework"
    - "a distributed block coordinate descent method for training l_1 regularized linear classifiers"

Connected through hyperedge HE-4 (induced by source paper "federated optimization distributed machine learning for on device intelligence"):
  with members:
    - "sdca without duality regularization and individual convexity"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed optimization beyond the datacenter"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "stop wasting my gradients practical svrg"
    - "distributed optimization with arbitrary local solvers"
    - "l1 regularized distributed optimization a communication efficient primal dual framework"

Connected through hyperedge HE-5 (induced by source paper "distributed asynchronous dual free stochastic dual coordinate ascent"):
  with members:
    - "sdca without duality regularization and individual convexity"
    - "asynchronous stochastic gradient descent with variance reduction for non convex optimization"
    - "adding vs averaging in distributed primal dual optimization"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "asynchronous distributed semi stochastic gradient optimization"
    - "sdca without duality"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "staleness aware async sgd for distributed deep learning"

Connected through hyperedge HE-6 (induced by source paper "primal dual rates and certificates"):
  with members:
    - "adding vs averaging in distributed primal dual optimization"
    - "linear convergence of the randomized feasible descent method under the weak strong convexity assumption"
    - "distributed optimization with arbitrary local solvers"
    - "sdca without duality"
    - "l1 regularized distributed optimization a communication efficient primal dual framework"
    - "mini batch primal and dual methods for svms"
    - "an equivalence between the lasso and support vector machines"
    - "duality between subgradient and conditional gradient methods"

Connected through hyperedge HE-7 (induced by source paper "a general distributed dual coordinate optimization framework for regularized loss minimization"):
  with members:
    - "cocoa a general framework for communication efficient distributed optimization"
    - "adding vs averaging in distributed primal dual optimization"
    - "distributed optimization with arbitrary local solvers"
    - "a distributed block coordinate descent method for training l_1 regularized linear classifiers"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "analysis of distributed stochastic dual coordinate ascent"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "an efficient distributed learning algorithm based on effective local functional approximations"

Connected through hyperedge HE-8 (induced by source paper "distributed inexact damped newton method data partitioning and load balancing"):
  with members:
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "distributed optimization with arbitrary local solvers"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "analysis of distributed stochastic dual coordinate ascent"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "parallel coordinate descent for l1 regularized loss minimization"
    - "optimal distributed online prediction using mini batches"

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)