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 neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
  Abstract: Memristive devices represent a promising technology for building neuromorphic electronic systems. In addition to their compactness and non-volatility, they are characterized by their computationally relevant physical properties, such as their state-dependence, non-linear conductance changes, and intrinsic variability in both their switching threshold and conductance values, that make them ideal devices for emulating the bio-physics of real synapses. In this paper we present a spiking neural network architecture that supports the use of memristive devices as synaptic elements and propose mixed-signal analog-digital interfacing circuits that mitigate the effect of variability in their conductance values and exploit their variability in the switching threshold for implementing stochastic learning. The effect of device variability is mitigated using pairs of memristive devices configured in a complementary push–pull mechanism and interfaced to a current-mode normalizer circuit. The stochastic learning mechanism is obtained by mapping the desired change in synaptic weight into a corresponding switching probability that is derived from the intrinsic stochastic behavior of memristive devices. We demonstrate the features of the CMOS circuits and apply the architecture proposed to a standard neural network hand-written digit classification benchmark based on the MNIST data-set. We evaluate the performance of the approach proposed in this benchmark using behavioral-level spiking neural network simulation, showing both the effect of the reduction in conductance variability produced by the current-mode normalizer circuit and the increase in performance as a function of the number of memristive devices used in each synapse.

Connected through hyperedge HE-1 (induced by source paper "a neuromorphic boost to rnns using low pass filters"):
  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-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"):
  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"
    - "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-3 (induced by source paper "error triggered three factor learning dynamics for crossbar arrays"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "smplr deep smpl reverse for 3d human pose and shape recovery"
    - "deep supervised learning using local errors"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "learning in the machine the symmetries of the deep learning channel"

Connected through hyperedge HE-4 (induced by source paper "an ultra low power sigma delta neuron circuit"):
  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"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"

Connected through hyperedge HE-5 (induced by source paper "memristors for the curious outsiders"):
  with members:
    - "hierarchical temporal memory using memristor networks a survey"
    - "thermodynamic ram technology stack"
    - "perspective memcomputing leveraging memory and physics to compute efficiently"
    - "the case for rejecting the memristor as a fundamental circuit element"
    - "vertex coloring of graphs via phase dynamics of coupled oscillatory networks"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"
    - "temporal correlation detection using computational phase change memory"
    - "optimal dictionary for least squares representation"

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)