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 neuromorphic boost to rnns using low pass filters"):
  Abstract: The increasing difficulty with Moore's law scaling and the remarkable success of machine learning have triggered a renaissance in the study of low-latency, energy-efficient accelerators for machine learning applications. In particular, spiking neural networks (SNNs) and their neuromorphic hardware implementations have started to receive substantial attention from academia and industry. However, SNNs perform relatively poorly compared to their rate-based counterparts, in terms of accuracy in pattern recognition tasks. In this paper, we present a low pass recurrent neural network (lpRNN) cell that can be trained using backpropagation and implemented on neuromorphic SNN devices. The ability to implement our model on neuromorphic hardware enables the construction of compact devices that can perform always-on processing in ultra-low power edge computing applications. We further argue that the low pass filter is a temporal regularizer and highlight its advantage in a Long Short-Term Memory (LSTM) cell. We show that low pass RNNs are able to learn and generalize better than their unfiltered variants on two long memory synthetic tasks, a character-level text modeling task, and a neuromorphic spoken command detection system.

Includes members:
  Member "an ultra low power sigma delta neuron circuit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mapping high performance rnns to in memory neuromorphic chips")
  Member "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "morphic a 65 nm 738k synapse mm 2 quad core binary weight digital neuromorphic processor with stochastic spike driven online learning")
  Member "independently recurrent neural network indrnn building a longer and deeper rnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "enabling the adoption of processing in memory challenges mechanisms future research directions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "machine learning systems for highly distributed and rapidly growing data")
  Member "overcoming the vanishing gradient problem in plain recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mapping high performance rnns to in memory neuromorphic chips")
  Member "speech commands a dataset for limited vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "blank language models")
  Member "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a supervised learning algorithm for multilayer spiking neural networks based on temporal coding toward energy efficient vlsi processor design")

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)