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 divide and conquer solver for kernel support vector machines"
  Abstract: The kernel support vector machine (SVM) is one of the most widely used classification methods; however, the amount of computation required becomes the bottleneck when facing millions of samples. In this paper, we propose and analyze a novel divide-and-conquer solver for kernel SVMs (DC-SVM). In the division step, we partition the kernel SVM problem into smaller subproblems by clustering the data, so that each subproblem can be solved independently and efficiently. We show theoretically that the support vectors identified by the subproblem solution are likely to be support vectors of the entire kernel SVM problem, provided that the problem is partitioned appropriately by kernel clustering. In the conquer step, the local solutions from the subproblems are used to initialize a global coordinate descent solver, which converges quickly as suggested by our analysis. By extending this idea, we develop a multilevel Divide-and-Conquer SVM algorithm with adaptive clustering and early prediction strategy, which outperforms state-of-the-art methods in terms of training speed, testing accuracy, and memory usage. As an example, on the covtype dataset with half-a-million samples, DC-SVM is 7 times faster than LIBSVM in obtaining the exact SVM solution (to within $10^{-6}$ relative error) which achieves 96.15% prediction accuracy. Moreover, with our proposed early prediction strategy, DC-SVM achieves about 96% accuracy in only 12 minutes, which is more than 100 times faster than LIBSVM.

Connected through hyperedge HE-1 (induced by source paper "faster support vector machines"):
  with members:
    - "graph partitioning with acyclicity constraints"
    - "memetic multilevel hypergraph partitioning"
    - "engineering multilevel support vector machines"
    - "a multilevel bilinear programming algorithm for the vertex separator problem"
    - "parallel graph partitioning for complex networks"
    - "partitioning complex networks via size constrained clustering"
    - "recent advances in graph partitioning"
    - "parallel graph decompositions using random shifts"

Connected through hyperedge HE-2 (induced by source paper "generalization properties of hyper rkhs and its application to out of sample extensions"):
  with members:
    - "local group invariant representations via orbit embeddings"
    - "deep kernel learning"
    - "a la carte learning fast kernels"
    - "convolutional kernel networks"

Connected through hyperedge HE-3 (induced by source paper "multi merge budget maintenance for stochastic gradient descent svm training"):
  with members:
    - "fast supervised hashing with decision trees for high dimensional data"

Connected through hyperedge HE-4 (induced by source paper "speeding up budgeted stochastic gradient descent svm training with precomputed golden section search"):
  with members:
    - "fast supervised hashing with decision trees for high dimensional data"

Connected through hyperedge HE-5 (induced by source paper "efficient divide and conquer classification based on parallel feature space decomposition for distributed systems"):
  with members:
    - "generalized principal component analysis gpca"

Connected through hyperedge HE-6 (induced by source paper "engineering multilevel support vector machines"):
  with members:
    - "a pso and pattern search based memetic algorithm for svms parameters optimization"
    - "advanced coarsening schemes for graph partitioning"
    - "fast response to infection spread and cyber attacks on large scale networks"
    - "relaxation based coarsening and multiscale graph organization"
    - "multiscale approach for the network compression friendly ordering"
    - "multi level algorithms for modularity clustering"

Connected through hyperedge HE-7 (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 distributed block coordinate descent method for training l_1 regularized linear classifiers"

Connected through hyperedge HE-8 (induced by source paper "on the complexity of learning with kernels"):
  with members:
    - "scalable kernel methods via doubly stochastic gradients"
    - "the kernelized stochastic batch perceptron"
    - "sharp analysis of low rank kernel matrix approximations"

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)