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 k modes algorithm for clustering"
  Abstract: Many clustering algorithms exist that estimate a cluster centroid, such as K-means, K-medoids or mean-shift, but no algorithm seems to exist that clusters data by returning exactly K meaningful modes. We propose a natural definition of a K-modes objective function by combining the notions of density and cluster assignment. The algorithm becomes K-means and K-medoids in the limit of very large and very small scales. Computationally, it is slightly slower than K-means but much faster than mean-shift or K-medoids. Unlike K-means, it is able to find centroids that are valid patterns, truly representative of a cluster, even with nonconvex clusters, and appears robust to outliers and misspecification of the scale and number of clusters.

Connected through hyperedge HE-1 (induced by source paper "scalable laplacian k modes"):
  with members:
    - "a review of mean shift algorithms for clustering"

Connected through hyperedge HE-2 (induced by source paper "model compression as constrained optimization with application to neural nets part ii quantization"):
  with members:
    - "model compression as constrained optimization with application to neural nets part i general framework"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "ternary weight networks"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "theano a python framework for fast computation of mathematical expressions"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "learning both weights and connections for efficient neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "kernel cuts mrf meets kernel spectral clustering"):
  with members:
    - "secrets of grabcut and kernel k means"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "volumetric bias in segmentation and reconstruction secrets and solutions"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "scalable kernel clustering approximate kernel k means"
    - "an information theoretic analysis of hard and soft assignment methods for clustering"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "a submodular supermodular procedure with applications to discriminative structure learning"

Connected through hyperedge HE-4 (induced by source paper "a review of mean shift algorithms for clustering"):
  with members:
    - "reconstruction of sequential data with density models"

Connected through hyperedge HE-5 (induced by source paper "the laplacian k modes algorithm for clustering"):
  with members:
    - "projection onto the probability simplex an efficient algorithm with a simple proof and an application"
    - "clustering by low rank doubly stochastic matrix decomposition"
    - "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)