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 "tuple oriented compression for large scale mini batch stochastic gradient descent"):
  Abstract: Data compression is a popular technique for improving the efficiency of data processing workloads such as SQL queries and more recently, machine learning (ML) with classical batch gradient methods. But the efficacy of such ideas for mini-batch stochastic gradient descent (MGD), arguably the workhorse algorithm of modern ML, is an open question. MGD's unique data access pattern renders prior art, including those designed for batch gradient methods, less effective. We fill this crucial research gap by proposing a new lossless compression scheme we call tuple-oriented compression (TOC) that is inspired by an unlikely source, the string/ text compression scheme Lempel-Ziv-Welch, but tailored to MGD in a way that preserves tuple boundaries within mini-batches. We then present a suite of novel compressed matrix operation execution techniques tailored to the TOC compression scheme that operate directly over the compressed data representation and avoid decompression overheads. An extensive empirical evaluation with real-world datasets shows that TOC consistently achieves substantial compression ratios by up to 51x and reduces runtimes for MGD workloads by up to 10.2x in popular ML systems.

Includes members:
  Member "cmsis nn efficient neural network kernels for arm cortex m cpus":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference")
  Member "a cost based optimizer for gradient descent optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic gradient descent on highly parallel architectures")
  Member "systematic evaluation of convolution neural network advances on the imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis")
  Member "towards linear algebra over normalized data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "bolt on differential privacy for scalable stochastic gradient descent based analytics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "an overview of gradient descent optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "mllib machine learning in apache spark":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi layer optimizations for end to end data analytics")

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.LG(Machine Learning)