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: "a distributed block coordinate descent method for training l_1 regularized linear classifiers"
  Abstract: Distributed training of $l_1$ regularized classifiers has received great attention recently. Most existing methods approach this problem by taking steps obtained from approximating the objective by a quadratic approximation that is decoupled at the individual variable level. These methods are designed for multicore and MPI platforms where communication costs are low. They are inefficient on systems such as Hadoop running on a cluster of commodity machines where communication costs are substantial. In this paper we design a distributed algorithm for $l_1$ regularization that is much better suited for such systems than existing algorithms. A careful cost analysis is used to support these points and motivate our method. The main idea of our algorithm is to do block optimization of many variables on the actual objective function within each computing node; this increases the computational cost per step that is matched with the communication cost, and decreases the number of outer iterations, thus yielding a faster overall method. Distributed Gauss-Seidel and Gauss-Southwell greedy schemes are used for choosing variables to update in each step. We establish global convergence theory for our algorithm, including Q-linear rate of convergence. Experiments on two benchmark problems show our method to be much faster than existing methods.

Connected through hyperedge HE-1 (induced by source paper "a distributed second order algorithm you can trust"):
  with members:
    - "giant globally improved approximate newton method for distributed optimization"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "a general distributed dual coordinate optimization framework for regularized loss minimization"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-2 (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"
    - "distributed mini batch sdca"
    - "l1 regularized distributed optimization a communication efficient primal dual framework"

Connected through hyperedge HE-3 (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"
    - "distributed mini batch sdca"
    - "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-4 (induced by source paper "communication efficient parallel block minimization for kernel machines"):
  with members:
    - "large scale kernel learning using block coordinate descent"
    - "a distributed algorithm for training nonlinear kernel machines"
    - "fastfood approximate kernel expansions in loglinear time"
    - "a divide and conquer solver for kernel support vector machines"

Connected through hyperedge HE-5 (induced by source paper "cocoa a general framework for communication efficient distributed optimization"):
  with members:
    - "distributed optimization with arbitrary local solvers"
    - "analysis of distributed stochastic dual coordinate ascent"

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)