human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "dot product join an array relation join operator for big model analytics"):
  Abstract: Big Data analytics has been approached exclusively from a data-parallel perspective, where data are partitioned to multiple workers ‐ threads or separate servers ‐ and model training is executed concurrently over different partitions, under various synchronization schemes that guarantee speedup and/or convergence. The dual ‐ Big Model ‐ problem that, surprisingly, has received no attention in database analytics, is how to manage models with millions if not billions of parameters that do not fit in memory. This distinction in model representation changes fundamentally how in-database analytics tasks are carried out. In this paper, we introduce the first secondary storage array-relation dot-product join operator between a set of sparse arrays and a dense relation targeted. The paramount challenge in designing such an operator is how to optimally schedule access to the dense relation based on the sparse non-contiguous entries in the sparse arrays. We prove that this problem is NP-hard and propose a practical solution characterized by two important technical contributions— dynamic batch processing and array reordering. We execute extensive experiments over synthetic and real data that confirm the minimal overhead the operator incurs when sufficient memory is available and the graceful degradation it suffers as memory resources become scarce. Moreover, dot-product join achieves an order of magnitude reduction in execution time for Big Model analytics over alternative in-database solutions.

Includes members:
  Member "mli an api for distributed machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tf replicator distributed machine learning for researchers")
  Member "performance evaluation of sparse matrix multiplication kernels on intel xeon phi":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prins resistive cam processing in storage")
  Member "the madlib analytics library or mad skills the sql":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "towards a unified architecture for in rdbms analytics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "a reliable effective terascale linear learning system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stannis low power acceleration of deep neural network training using computational storage")
  Member "fast sparse matrix vector multiplication on gpus implications for graph mining":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "slide in defense of smart algorithms over hardware acceleration for large scale deep learning systems")

Question: Please tell me which class the source paper 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.DB(Databases)