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: "matrix computations and optimization in apache spark"
  Abstract: We describe matrix computations available in the cluster programming framework, Apache Spark. Out of the box, Spark provides abstractions and implementations for distributed matrices and optimization routines using these matrices. When translating single-node algorithms to run on a distributed cluster, we observe that often a simple idea is enough: separating matrix operations from vector operations and shipping the matrix operations to be ran on the cluster, while keeping vector operations local to the driver. In the case of the Singular Value Decomposition, by taking this idea to an extreme, we are able to exploit the computational power of a cluster, while running code written decades ago for a single core. Another example is our Spark port of the popular TFOCS optimization package, originally built for MATLAB, which allows for solving Linear programs as well as a variety of other convex programs. We conclude with a comprehensive set of benchmarks for hardware accelerated matrix computations from the JVM, which is interesting in its own right, as many cluster programming frameworks use the JVM. The contributions described in this paper are already merged into Apache Spark and available on Spark installations by default, and commercially supported by a slew of companies which provide further services.

Connected through hyperedge HE-1 (induced by source paper "caddelag framework for distributed anomaly detection in large dense graph sequences"):
  with members:
    - "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"
    - "large scale spectral clustering using resistance distance and spielman teng solvers"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"

Connected through hyperedge HE-2 (induced by source paper "fusionnet 3d object classification using multiple data representations"):
  with members:
    - "pairwise decomposition of image sequences for active multi view recognition"
    - "how transferable are features in deep neural networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "3d shapenets a deep representation for volumetric shapes"
    - "caffe convolutional architecture for fast feature embedding"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "dimension independent matrix square using mapreduce"

Connected through hyperedge HE-3 (induced by source paper "matrix factorization at scale a comparison of scientific data analytics in spark and c mpi using three case studies"):
  with members:
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "a high performance parallel algorithm for nonnegative matrix factorization"
    - "mllib machine learning in apache spark"
    - "scalable methods for nonnegative matrix factorizations of near separable tall and skinny matrices"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"
    - "randomized algorithms for matrices and data"
    - "block locally optimal preconditioned eigenvalue xolvers blopex in hypre and petsc"
    - "relative error cur matrix decompositions"

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.DC(Distributed, Parallel, and Cluster Computing)