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: "neuromorphic electronic circuits for building autonomous cognitive systems"
  Abstract: Several analog and digital brain-inspired electronic systems have been recently proposed as dedicated solutions for fast simulations of spiking neural networks. While these architectures are useful for exploring the computational properties of large-scale models of the nervous system, the challenge of building low-power compact physical artifacts that can behave intelligently in the real world and exhibit cognitive abilities still remains open. In this paper, we propose a set of neuromorphic engineering solutions to address this challenge. In particular, we review neuromorphic circuits for emulating neural and synaptic dynamics in real time and discuss the role of biophysically realistic temporal dynamics in hardware neural processing architectures; we review the challenges of realizing spike-based plasticity mechanisms in real physical systems and present examples of analog electronic circuits that implement them;we describe the computational properties of recurrent neural networks and show how neuromorphic winner-take-all circuits can implement working-memory and decision-making mechanisms. We validate the neuromorphic approach proposed with experimental results obtained from our own circuits and systems, and argue how the circuits and networks presented in this work represent a useful set of components for efficiently and elegantly implementing neuromorphic cognition.

Connected through hyperedge HE-1 (induced by source paper "synaptic integration of spatiotemporal features with a dynamic neuromorphic processor"):
  with members:
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "an accelerated analog neuromorphic hardware system emulating nmda and calcium based non linear dendrites"

Connected through hyperedge HE-2 (induced by source paper "spiking neural networks for inference and learning a memristor based design perspective"):
  with members:
    - "on resistive memories one step row readout technique and sensing circuitry"
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "surrogate gradient learning in spiking neural networks"
    - "contrastive hebbian learning with random feedback weights"
    - "mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "slayer spike layer error reassignment in time"
    - "deep supervised learning using local errors"

Connected through hyperedge HE-3 (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"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"

Connected through hyperedge HE-4 (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"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"

Connected through hyperedge HE-5 (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"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "trained ternary quantization"
    - "convolutional networks for fast energy efficient neuromorphic computing"

Connected through hyperedge HE-6 (induced by source paper "a spiking network for inference of relations trained with neuromorphic backpropagation"):
  with members:
    - "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "algorithm and hardware design of discrete time spiking neural networks based on back propagation with binary activations"
    - "a wake sleep algorithm for recurrent spiking neural networks"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "learning and inferring relations in cortical networks"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-7 (induced by source paper "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"):
  with members:
    - "neuromorphic computing with multi memristive synapses"
    - "an ultralow leakage synaptic scaling homeostatic plasticity circuit with configurable time scales up to 100 ks"
    - "a differential memristive synapse circuit for on line learning in neuromorphic computing systems"
    - "stochastic synapses enable efficient brain inspired learning machines"
    - "deep learning in neural networks"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "integration of nanoscale memristor synapses in neuromorphic computing architectures"

Connected through hyperedge HE-8 (induced by source paper "parameter optimization and learning in a spiking neural network for uav obstacle avoidance targeting neuromorphic processors"):
  with members:
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "rank pooling for action recognition"
    - "the treasure beneath convolutional layers cross convolutional layer pooling for image classification"
    - "evolution of plastic learning in spiking networks via memristive connections"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

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.ET(Emerging Technologies)