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: "large scale spectral clustering using resistance distance and spielman teng solvers"
  Abstract: The promise of spectral clustering is that it can help detect complex shapes and intrinsic manifold structure in large and high dimensional spaces. The price for this promise is the computational cost O(n 3) for computing the eigen-decomposition of the graph Laplacian matrix - so far a necessary subroutine for spectral clustering. In this paper we bypass the eigen-decomposition of the original Laplacian matrix by leveraging the recently introduced Spielman and Teng near-linear time solver for systems of linear equations and random projection. Experiments on several synthetic and real datasets show that the proposed approach has better clustering quality and is faster than the state-of-the-art approximate spectral clustering methods.

Connected through hyperedge HE-1 (induced by source paper "change detection in dynamic attributed networks"):
  with members:
    - "anomaly detection in online social networks"
    - "interpreting communities based on the evolution of a dynamic attributed network"
    - "link prediction in networks with nodes attributes by similarity propagation"
    - "detecting change points in the large scale structure of evolving networks"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-2 (induced by source paper "caddelag framework for distributed anomaly detection in large dense graph sequences"):
  with members:
    - "matrix computations and optimization in apache spark"
    - "a fast distributed solver for symmetric diagonally dominant linear equations"
    - "powers of tensors and fast matrix multiplication"
    - "graph based anomaly detection and description a survey"
    - "deltacon a principled massive graph similarity function"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"

Connected through hyperedge HE-3 (induced by source paper "machine learning friendly set version of johnson lindenstrauss lemma"):
  with members:
    - "optimality of the johnson lindenstrauss lemma"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "compressive spectral clustering"
    - "random sampling of bandlimited signals on graphs"
    - "computational feasibility of clustering under clusterability assumptions"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "low rank approximation and regression in input sparsity time"
    - "clustering under perturbation resilience"

Connected through hyperedge HE-4 (induced by source paper "on the structure and computation of random walk times in finite graphs"):
  with members:
    - "optimal temporal logic planning in probabilistic semantic maps"
    - "minimizing convergence error in multi agent systems via leader selection a supermodular optimization approach"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"
    - "opinion dynamics in social networks a local interaction game with stubborn agents"
    - "mdp optimal control under temporal logic constraints"

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)