human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
  Abstract: Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantages in terms of bandwidth and power consumption in neuromorphic electronic systems. However, managing the traffic of asynchronous events in large scale systems is a daunting task, both in terms of circuit complexity and memory requirements. Here, we present a novel routing methodology that employs both hierarchical and mesh routing strategies and combines heterogeneous memory structures for minimizing both memory requirements and latency, while maximizing programming flexibility to support a wide range of event-based neural network architectures, through parameter configuration. We validated the proposed scheme in a prototype multicore neuromorphic processor chip that employs hybrid analog/digital circuits for emulating synapse and neuron dynamics together with asynchronous digital circuits for managing the address-event traffic. We present a theoretical analysis of the proposed connectivity scheme, describe the methods and circuits used to implement such scheme, and characterize the prototype chip. Finally, we demonstrate the use of the neuromorphic processor with a convolutional neural network for the real-time classification of visual symbols being flashed to a dynamic vision sensor (DVS) at high speed.

Connected through hyperedge HE-1 (induced by source paper "a supervised learning algorithm for multilayer spiking neural networks based on temporal coding toward energy efficient vlsi processor design"):
  with members:
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "deep learning in spiking neural networks"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"
    - "an energy efficient time domain analog vlsi neural network processor based on a pulse width modulation approach"
    - "supervised learning in spiking neural networks with phase change memory synapses"
    - "a time domain analog weighted sum calculation model for extremely low power vlsi implementation of multi layer neural networks"
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"

Connected through hyperedge HE-2 (induced by source paper "event based angular velocity regression with spiking networks"):
  with members:
    - "deep learning in spiking neural networks"
    - "end to end learning of representations for asynchronous event based data"
    - "event based vision a survey"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "slayer spike layer error reassignment in time"

Connected through hyperedge HE-3 (induced by source paper "design space exploration of hardware spiking neurons for embedded artificial intelligence"):
  with members:
    - "deep learning in spiking neural networks"
    - "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "hfirst a temporal approach to object recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "synaptic integration of spatiotemporal features with a dynamic neuromorphic processor"):
  with members:
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "an accelerated analog neuromorphic hardware system emulating nmda and calcium based non linear dendrites"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"

Connected through hyperedge HE-5 (induced by source paper "event based vision a survey"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "atis spinnaker a fully event based visual tracking demonstration"
    - "ced color event camera dataset"
    - "unsupervised learning of a hierarchical spiking neural network for optical flow estimation from events to global motion perception"
    - "high speed and high dynamic range video with an event camera"
    - "end to end learning of representations for asynchronous event based data"
    - "speed invariant time surface for learning to detect corner points with event based cameras"

Connected through hyperedge HE-6 (induced by source paper "mapping high performance rnns to in memory neuromorphic chips"):
  with members:
    - "an ultra low power sigma delta neuron circuit"
    - "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "overcoming the vanishing gradient problem in plain recurrent networks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "origami a 803 gop s w convolutional network accelerator"

Connected through hyperedge HE-7 (induced by source paper "a neuromorphic boost to rnns using low pass filters"):
  with members:
    - "an ultra low power sigma delta neuron circuit"
    - "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "overcoming the vanishing gradient problem in plain recurrent networks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "origami a 803 gop s w convolutional network accelerator"

Connected through hyperedge HE-8 (induced by source paper "harnessing slow dynamics in neuromorphic computation"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "analog circuits for mixed signal neuromorphic computing architectures in 28 nm fd soi technology"
    - "real time closed loop neural decoding on a neuromorphic chip"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "slayer spike layer error reassignment in time"
    - "trained ternary quantization"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "memory and information processing in neuromorphic systems"

Question: Please tell me which class the center node 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.AR(Hardware Architecture)