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 "minibatch processing in spiking neural networks"):
  Abstract: Spiking neural networks (SNNs) are a promising candidate for biologically-inspired and energy efficient computation. However, their simulation is notoriously time consuming, and may be seen as a bottleneck in developing competitive training methods with potential deployment on neuromorphic hardware platforms. To address this issue, we provide an implementation of mini-batch processing applied to clock-based SNN simulation, leading to drastically increased data throughput. To our knowledge, this is the first general-purpose implementation of mini-batch processing in a spiking neural networks simulator, which works with arbitrary neuron and synapse models. We demonstrate nearly constant-time scaling with batch size on a simulation setup (up to GPU memory limits), and showcase the effectiveness of large batch sizes in two SNN application domains, resulting in $\approx$880X and $\approx$24X reductions in wall-clock time respectively. Different parameter reduction techniques are shown to produce different learning outcomes in a simulation of networks trained with spike-timing-dependent plasticity. Machine learning practitioners and biological modelers alike may benefit from the drastically reduced simulation time and increased iteration speed this method enables. Code to reproduce the benchmarks and experimental findings in this paper can be found at this https URL.

Includes members:
  Member "improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to atari games":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "admiring the great mountain a celebration special issue in honor of stephen grossbergs 80th birthday")
  Member "deep learning in spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a supervised learning algorithm for multilayer spiking neural networks based on temporal coding toward energy efficient vlsi processor design")
  Member "temporal coding in spiking neural networks with alpha synaptic function":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification")
  Member "surrogate gradient learning in spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rmp snns residual membrane potential neuron for enabling deeper high accuracy and low latency spiking neural networks")
  Member "nengodl combining deep learning and neuromorphic modelling methods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a spike in performance training hybrid spiking neural networks with quantized activation functions")
  Member "going deeper in spiking neural networks vgg and residual architectures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rmp snns residual membrane potential neuron for enabling deeper high accuracy and low latency spiking neural networks")
  Member "bindsnet a machine learning oriented spiking neural networks library in python":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron")
  Member "revisiting small batch training for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hyper parameter optimization a review of algorithms and applications")

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)