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 gaussian belief propagation solver for large scale support vector machines"
  Abstract: Support vector machines (SVMs) are an extremely successful type of classification and regression algorithms. Building an SVM entails solving a constrained convex quadratic programming problem, which is quadratic in the number of training samples. We introduce an efficient parallel implementation of an support vector regression solver, based on the Gaussian Belief Propagation algorithm (GaBP). #R##N#In this paper, we demonstrate that methods from the complex system domain could be utilized for performing efficient distributed computation. We compare the proposed algorithm to previously proposed distributed and single-node SVM solvers. Our comparison shows that the proposed algorithm is just as accurate as these solvers, while being significantly faster, especially for large datasets. We demonstrate scalability of the proposed algorithm to up to 1,024 computing nodes and hundreds of thousands of data points using an IBM Blue Gene supercomputer. As far as we know, our work is the largest parallel implementation of belief propagation ever done, demonstrating the applicability of this algorithm for large scale distributed computing systems.

Connected through hyperedge HE-1 (induced by source paper "fixing convergence of gaussian belief propagation"):
  with members:
    - "gaussian belief propagation based multiuser detection"
    - "gaussian belief propagation solver for systems of linear equations"
    - "distributed kalman filter via gaussian belief propagation"
    - "polynomial linear programming with gaussian belief propagation"
    - "belief propagation based multi user detection"

Connected through hyperedge HE-2 (induced by source paper "gaussian belief propagation theory and application"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "distributed large scale network utility maximization"
    - "gaussian belief propagation based multiuser detection"
    - "gaussian belief propagation solver for systems of linear equations"
    - "distributed kalman filter via gaussian belief propagation"
    - "polynomial linear programming with gaussian belief propagation"

Connected through hyperedge HE-3 (induced by source paper "gaussian belief propagation for solving systems of linear equations theory and application"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "gaussian belief propagation based multiuser detection"
    - "gaussian belief propagation solver for systems of linear equations"
    - "analysis of belief propagation for non linear problems the example of cdma or how to prove tanaka s formula"
    - "belief propagation based multi user detection"

Connected through hyperedge HE-4 (induced by source paper "polynomial linear programming with gaussian belief propagation"):
  with members:
    - "convergent message passing algorithms for inference over general graphs with convex free energies"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "gaussian belief propagation based multiuser detection"
    - "gaussian belief propagation solver for systems of linear equations"

Connected through hyperedge HE-5 (induced by source paper "distributed kalman filter via gaussian belief propagation"):
  with members:
    - "gaussian belief propagation based multiuser detection"
    - "gaussian belief propagation solver for systems of linear equations"
    - "polynomial linear programming with gaussian belief propagation"

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)