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: "integration of nanoscale memristor synapses in neuromorphic computing architectures"
  Abstract: Conventional neuro-computing architectures and artificial neural networks have often been developed with no or loose connections to neuroscience. As a consequence, they have largely ignored key features of biological neural processing systems, such as their extremely low-power consumption features or their ability to carry out robust and efficient computation using massively parallel arrays of limited precision, highly variable, and unreliable components. Recent developments in nano-technologies are making available extremely compact and low-power, but also variable and unreliable solid-state devices that can potentially extend the offerings of availing CMOS technologies. In particular, memristors are regarded as a promising solution for modeling key features of biological synapses due to their nanoscale dimensions, their capacity to store multiple bits of information per element and the low energy required to write distinct states. In this paper, we first review the neuro- and neuromorphic-computing approaches that can best exploit the properties of memristor and-scale devices, and then propose a novel hybrid memristor-CMOS neuromorphic circuit which represents a radical departure from conventional neuro-computing approaches, as it uses memristors to directly emulate the biophysics and temporal dynamics of real synapses. We point out the differences between the use of memristors in conventional neuro-computing architectures and the hybrid memristor-CMOS circuit proposed, and argue how this circuit represents an ideal building block for implementing brain-inspired probabilistic computing paradigms that are robust to variability and fault-tolerant by design.

Connected through hyperedge HE-1 (induced by source paper "recent advances in physical reservoir computing a review"):
  with members:
    - "distributive dynamic spectrum access through deep reinforcement learning a reservoir computing based approach"
    - "echo state networks are universal"
    - "reservoir computing hardware with cellular automata"
    - "scaling up echo state networks with multiple light scattering"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "reservoir computing universality with stochastic inputs"
    - "the case for rejecting the memristor as a fundamental circuit element"
    - "reservoir computing using non uniform binary cellular automata"

Connected through hyperedge HE-2 (induced by source paper "fpsa a full system stack solution for reconfigurable reram based nn accelerator architecture"):
  with members:
    - "eie efficient inference engine on compressed deep neural network"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (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"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"

Connected through hyperedge HE-4 (induced by source paper "the impact of on chip communication on memory technologies for neuromorphic systems"):
  with members:
    - "on chip face recognition system design with memristive hierarchical temporal memory"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "energy efficient hybrid cmos nems lif neuron circuit in 28 nm cmos process"

Connected through hyperedge HE-5 (induced by source paper "low power artificial neural network architecture"):
  with members:
    - "a differential memristive synapse circuit for on line learning in neuromorphic computing systems"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-6 (induced by source paper "energy efficient cmos memristive synapses for mixed signal neuromorphic system on a chip"):
  with members:
    - "a compact cmos memristor emulator circuit and its applications"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "enabling bio plausible multi level stdp using cmos neurons with dendrites and bistable rrams"
    - "a cmos spiking neuron for brain inspired neural networks with resistive synapses and in situ learning"
    - "homogeneous spiking neuromorphic system for real world pattern recognition"

Connected through hyperedge HE-7 (induced by source paper "neuromorphic computing with multi memristive synapses"):
  with members:
    - "mixed precision in memory computing"
    - "temporal correlation detection using computational phase change memory"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "brain like associative learning using a nanoscale non volatile phase change synaptic device array"

Connected through hyperedge HE-8 (induced by source paper "perceptrons from memristors"):
  with members:
    - "analog simulator of integro differential equations with classical memristors"
    - "universal memcomputing machines"
    - "an approximate backpropagation learning rule for memristor based neural networks using synaptic plasticity"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

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)