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 "optimally scheduling cnn convolutions for efficient memory access"):
  Abstract: Embedded inference engines for convolutional networks must be parsimonious in memory bandwidth and buffer sizing to meet power and cost constraints. We present an analytical memory bandwidth model for loop-nest optimization targeting architectures with application managed buffers. We applied this model to optimize the CNN convolution loop-nest. We show that our model is more accurate than previously published models. Using this model we can identify non-trivial dataflow schedules that result in lowest communication bandwidth given tight local buffering constraints. We show that optimal dataflow schedules are implementable in practice and that our model is accurate with respect to a real implementation; moreover, we introduce an accelerator architecture, named Hardware Convolution Block (HWC), which implements the optimal schedules, and we show it achieves up to 14x memory bandwidth reduction compared to a previously published accelerator with a similar memory interface, but implementing a non-optimal schedule.

Includes members:
  Member "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors")
  Member "neurostream scalable and energy efficient deep learning with smart memory cubes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a unified optimization approach for cnn model inference on integrated gpus")
  Member "extended bit plane compression for convolutional neural network accelerators":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ebpc extended bit plane compression for deep neural network inference and training accelerators")
  Member "an iot endpoint system on chip for secure and energy efficient near sensor analytics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors")
  Member "efficient processing of deep neural networks a tutorial and survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "searching for winograd aware quantized networks")
  Member "the power of sparsity in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "what is the state of neural network pruning")
  Member "a survey of model compression and acceleration for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "a systematic approach to blocking convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "marvel a decoupled model driven approach for efficiently mapping convolutions on spatial dnn accelerators")

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.NE(Neural and Evolutionary Computing)