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: "the anchors hierachy using the triangle inequality to survive high dimensional data"
  Abstract: This paper is about metric data structures in high-dimensional or non-Euclidean space that permit cached sufficient statistics accelerations of learning algorithms. #R##N#It has recently been shown that for less than about 10 dimensions, decorating kd-trees with additional "cached sufficient statistics" such as first and second moments and contingency tables can provide satisfying acceleration for a very wide range of statistical learning tasks such as kernel regression, locally weighted regression, k-means clustering, mixture modeling and Bayes Net learning. #R##N#In this paper, we begin by defining the anchors hierarchy - a fast data structure and algorithm for localizing data based only on a triangle-inequality-obeying distance metric. We show how this, in its own right, gives a fast and effective clustering of data. But more importantly we show how it can produce a well-balanced structure similar to a Ball-Tree (Omohundro, 1991) or a kind of metric tree (Uhlmann, 1991; Ciaccia, Patella, & Zezula, 1997) in a way that is neither "top-down" nor "bottom-up" but instead "middle-out". We then show how this structure, decorated with cached sufficient statistics, allows a wide variety of statistical learning algorithms to be accelerated even in thousands of dimensions.

Connected through hyperedge HE-1 (induced by source paper "plug and play dual tree algorithm runtime analysis"):
  with members:
    - "tree independent dual tree algorithms"
    - "variational dual tree framework for large scale transition matrix approximation"
    - "mlpack a scalable c machine learning library"
    - "faster gaussian summation theory and experiment"
    - "fast exact max kernel search"

Connected through hyperedge HE-2 (induced by source paper "ball tree efficient spatial indexing for constrained nearest neighbor search in metric spaces"):
  with members:
    - "performance evaluation ball treeand kd tree in the context of mst"
    - "survey of nearest neighbor techniques"

Connected through hyperedge HE-3 (induced by source paper "mix nets factored mixtures of gaussians in bayesian networks with mixed continuous and discrete variables"):
  with members:
    - "nonuniform dynamic discretization in hybrid networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning bayesian networks with local structure"
    - "a multivariate discretization method for learning bayesian networks from mixed data"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "inference using message propagation and topology transformation in vector gaussian continuous networks"
    - "models and selection criteria for regression and classification"

Connected through hyperedge HE-4 (induced by source paper "the bregman variational dual tree framework"):
  with members:
    - "variational dual tree framework for large scale transition matrix approximation"
    - "agglomerative bregman clustering"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "fast neighborhood graph search using cartesian concatenation"):
  with members:
    - "fast approximate k means via cluster closures"
    - "searching in one billion vectors re rank with source coding"

Connected through hyperedge HE-6 (induced by source paper "fast exact max kernel search"):
  with members:
    - "mlpack a scalable c machine learning library"
    - "accelerating nearest neighbor search on manycore systems"
    - "beyond sentiment the manifold of human emotions"

Connected through hyperedge HE-7 (induced by source paper "variational dual tree framework for large scale transition matrix approximation"):
  with members:
    - "a tutorial on spectral clustering"

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)