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 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"
  Abstract: Shifting computing architectures from von Neumann to event-based spiking neural networks (SNNs) uncovers new opportunities for low-power processing of sensory data in applications such as vision or sensorimotor control. Exploring roads toward cognitive SNNs requires the design of compact, low-power and versatile experimentation platforms with the key requirement of online learning in order to adapt and learn new features in uncontrolled environments. However, embedding online learning in SNNs is currently hindered by high incurred complexity and area overheads. In this paper, we present ODIN, a 0.086-mm  $^2$   64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm FDSOI CMOS achieving a minimum energy per synaptic operation (SOP) of 12.7 pJ. It leverages an efficient implementation of the spike-driven synaptic plasticity (SDSP) learning rule for high-density embedded online learning with only 0.68   $\mu$  m  $^2$   per 4-bit synapse. Neurons can be independently configured as a standard leaky integrate-and-fire model or as a custom phenomenological model that emulates the 20 Izhikevich behaviors found in biological spiking neurons. Using a single presentation of 6k 16 × 16 MNIST training images to a single-layer fully-connected 10-neuron network with on-chip SDSP-based learning, ODIN achieves a classification accuracy of 84.5%, while consuming only 15 nJ/inference at 0.55 V using rank order coding. ODIN thus enables further developments toward cognitive neuromorphic devices for low-power, adaptive and low-cost processing.

Connected through hyperedge HE-1 (induced by source paper "an internal clock based space time neural network for motion speed recognition"):
  with members:
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep learning and face recognition the state of the art"

Connected through hyperedge HE-2 (induced by source paper "mapping high performance rnns to in memory neuromorphic chips"):
  with members:
    - "an ultra low power sigma delta neuron circuit"
    - "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"
    - "origami a 803 gop s w convolutional network accelerator"

Connected through hyperedge HE-3 (induced by source paper "a spiking network for inference of relations trained with neuromorphic backpropagation"):
  with members:
    - "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"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (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"):
  with members:
    - "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
    - "binareye an always on energy accuracy scalable binary cnn processor with all memory on chip in 28nm cmos"
    - "minimum energy quantized 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"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "a biological realtime neuromorphic system in 28 nm cmos using low leakage switched capacitor circuits"

Connected through hyperedge HE-5 (induced by source paper "low power neuromorphic hardware for signal processing applications"):
  with members:
    - "neuromorphic computing with multi memristive synapses"
    - "deep networks incorporating spiking neural dynamics"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "learning to recognize actions from limited training examples using a recurrent spiking neural model"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-6 (induced by source paper "efficient reward based structural plasticity on a spinnaker 2 prototype"):
  with members:
    - "an fpga based massively parallel neuromorphic cortex simulator"
    - "large scale neuromorphic spiking array processors a quest to mimic the brain"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "neuromorphic hardware in the loop training a deep spiking network on the brainscales wafer scale system"
    - "learning both weights and connections for efficient neural networks"
    - "a biological realtime neuromorphic system in 28 nm cmos using low leakage switched capacitor circuits"

Connected through hyperedge HE-7 (induced by source paper "the importance of space and time in neuromorphic cognitive agents"):
  with members:
    - "an ultralow leakage synaptic scaling homeostatic plasticity circuit with configurable time scales up to 100 ks"
    - "adaption toolbox and benchmark for training convolutional neural networks with reduced numerical precision weights and activation"
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"

Connected through hyperedge HE-8 (induced by source paper "an ultra low power sigma delta neuron circuit"):
  with members:
    - "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
    - "neuromorphic electronic circuits for building autonomous cognitive 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.ET(Emerging Technologies)