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 "towards accurate and high speed spiking neuromorphic systems with data quantization aware deep networks"):
  Abstract: Deep Neural Networks (DNNs) have gained immense success in cognitive applications and greatly pushed today’s artificial intelligence forward. The biggest challenge in executing DNNs is their extremely data-extensive computations. The computing efficiency in speed and energy is constrained when traditional computing platforms are employed in such computational hungry executions. Spiking neuromorphic computing (SNC) has been widely investigated in deep networks implementation own to their high efficiency in computation and communication. However, weights and signals of DNNs are required to be quantized when deploying the DNNs on the SNC, which results in unacceptable accuracy loss. Previous works mainly focus on weights discretize while inter-layer signals are mainly neglected. In this work, we propose to represent DNNs with fixed integer inter-layer signals and fixed-point weights while holding good accuracy. We implement the proposed DNNs on the memristor-based SNC system as a deployment example. With 4-bit data representation, our results show that the accuracy loss can be controlled within 0.02% (2.3%) on MNIST (CIFAR-10). Compared with the 8-bit dynamic fixed-point DNNs, our system can achieve more than 9.8× speedup, 89.1% energy saving, and 30% area saving.

Includes members:
  Member "group scissor scaling neuromorphic computing design to large neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hypar towards hybrid parallelism for deep learning accelerator array")
  Member "classification accuracy improvement for neuromorphic computing systems with one level precision synapses":
    also connected through other hyperedges:
      (none)
  Member "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement co learning of deep and spiking neural networks for energy efficient mapless navigation with neuromorphic hardware")
  Member "hardware software codesign of accurate multiplier free deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "alert accurate anytime learning for energy and timeliness")
  Member "hardware oriented approximation of convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "light multi segment activation for model compression")
  Member "eie efficient inference engine on compressed deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications")
  Member "xnor net imagenet classification using binary convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "convolutional networks for fast energy efficient neuromorphic computing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rmp snns residual membrane potential neuron for enabling deeper high accuracy and low latency spiking neural networks")

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.CV(Computer Vision and Pattern Recognition)