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 "a scalable near memory architecture for training deep neural networks on large in memory datasets"):
  Abstract: Most investigations into near-memory hardware accelerators for deep neural networks have primarily focused on inference, while the potential of accelerating training has received relatively little attention so far. Based on an in-depth analysis of the key computational patterns in state-of-the-art gradient-based training methods, we propose an efficient near-memory acceleration engine called NTX that can be used to train state-of-the-art deep convolutional neural networks at scale. Our main contributions are: (i) identifying requirements for efficient data address generation and developing an efficient accelerator offloading scheme reducing overhead by 7x over previously published results; (ii) support a rich set of operations allowing for efficient calculation of the back-propagation phase. The low control overhead allows up to 8 NTX engines to be controlled by a simple processor. Evaluations in a near-memory computing scenario where the accelerator is placed on the logic base die of a Hybrid Memory Cube demonstrate a 2.6x energy efficiency improvement over contemporary GPUs at 4.4x less silicon area, and an average compute performance of 1.01 Tflop/s for training large state-of-the-art networks with full floating-point precision. The architecture is scalable and paves the way towards efficient deep learning in a distributed near-memory setting.

Includes members:
  Member "neurostream scalable and energy efficient deep learning with smart memory cubes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a unified optimization approach for cnn model inference on integrated gpus")
  Member "efficient processing of deep neural networks a tutorial and survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "searching for winograd aware quantized networks")
  Member "flexpoint an adaptive numerical format for efficient training of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks")
  Member "learning structured sparsity in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "optimizing performance of recurrent neural networks on gpus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "all smiles variational autoencoder")
  Member "deep image scaling up image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic optimization of plain convolutional neural networks with simple methods")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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.DC(Distributed, Parallel, and Cluster Computing)