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: "fast sparse matrix vector multiplication on gpus implications for graph mining"
  Abstract: Scaling up the sparse matrix-vector multiplication kernel on modern Graphics Processing Units (GPU) has been at the heart of numerous studies in both academia and industry. In this article we present a novel non-parametric, self-tunable, approach to data representation for computing this kernel, particularly targeting sparse matrices representing power-law graphs. Using real data, we show how our representation scheme, coupled with a novel tiling algorithm, can yield significant benefits over the current state of the art GPU efforts on a number of core data mining algorithms such as PageRank, HITS and Random Walk with Restart.

Connected through hyperedge HE-1 (induced by source paper "slide in defense of smart algorithms over hardware acceleration for large scale deep learning systems"):
  with members:
    - "lsh sampling breaks the computation chicken and egg loop in adaptive stochastic gradient estimation"
    - "data curation with deep learning vision towards self driving data curation"
    - "auto tuning tensorflow threading model for cpu backend"
    - "scaling up split merge mcmc with locality sensitive sampling lss"
    - "in datacenter performance analysis of a tensor processing unit"
    - "flash randomized algorithms accelerated over cpu gpu for ultra high dimensional similarity search"
    - "adaptive sampled softmax with kernel based sampling"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "optimizing sparse matrix vector multiplication on emerging many core architectures"):
  with members:
    - "machine learning in compiler optimisation"
    - "an empirical study of intel xeon phi"

Connected through hyperedge HE-3 (induced by source paper "technical report accelerating dynamic graph analytics on gpus"):
  with members:
    - "tracking influential individuals in dynamic networks"
    - "snap a general purpose network analysis and graph mining library"
    - "approximate personalized pagerank on dynamic graphs"
    - "gunrock a high performance graph processing library on the gpu"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-4 (induced by source paper "pmv pre partitioned generalized matrix vector multiplication for scalable graph mining"):
  with members:
    - "pregelix big ger graph analytics on a dataflow engine"
    - "distributed graphlab a framework for machine learning in the cloud"
    - "hadoop performance models"

Connected through hyperedge HE-5 (induced by source paper "dot product join an array relation join operator for big model analytics"):
  with members:
    - "mli an api for distributed machine learning"
    - "performance evaluation of sparse matrix multiplication kernels on intel xeon phi"
    - "the madlib analytics library or mad skills the sql"
    - "towards a unified architecture for in rdbms analytics"
    - "a reliable effective terascale linear learning system"

Connected through hyperedge HE-6 (induced by source paper "dimmwitted a study of main memory statistical analytics"):
  with members:
    - "an approximate efficient solver for lp rounding"
    - "near optimal coresets for least squares regression"
    - "mli an api for distributed machine learning"
    - "massively parallel sort merge joins in main memory multi core database systems"
    - "the madlib analytics library or mad skills the sql"
    - "distributed graphlab a framework for machine learning in the cloud"
    - "building high level features using large scale unsupervised learning"
    - "a reliable effective terascale linear learning system"

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.NA(Numerical Analysis)