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: "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
  Abstract: The advances in the field of machine learning using neuromorphic systems have paved the pathway for extensive research on possibilities of hardware implementations of neural networks. Various memristive technologies such as oxide-based devices, spintronics, and phase change materials have been explored to implement the core functional units of neuromorphic systems, namely the synaptic network, and the neuronal functionality, in a fast and energy efficient manner. However, various nonidealities in the crossbar implementations of the synaptic arrays can significantly degrade performance of neural networks, and hence, impose restrictions on feasible crossbar sizes. In this paper, we build mathematical models of various nonidealities that occur in crossbar implementations such as source resistance, neuron resistance, and chip-to-chip device variations and analyze their impact on the classification accuracy of a fully connected network (FCN) and convolutional neural network (CNN) trained with Backpropagation algorithm. We show that a network trained under ideal conditions can suffer accuracy degradation as large as 59.84% for FCNs and 62.4% for CNNs when implemented on nonideal crossbars for relevant nonideality ranges. This severely constrains the sizes for crossbars. As a solution, we propose a technology aware training algorithm, which incorporates the mathematical models of the nonidealities in the backpropagation algorithm. We demonstrate that our proposed methodology achieves significant recovery of testing accuracy within 1.9% of the ideal accuracy for FCNs and 1.5% for CNNs. We further show that our proposed training algorithm can potentially allow the use of significantly larger crossbar arrays of sizes 784 × 500 for FCNs and 4096 × 512 for CNNs with a minor or no tradeoff in accuracy.

Connected through hyperedge HE-1 (induced by source paper "deep learning in memristive nanowire networks"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "attention is all you need"
    - "noisy activation functions"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "wide residual networks"
    - "exploring the limits of language modeling"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "txsim modeling training of deep neural networks on resistive crossbar systems"):
  with members:
    - "training large scale anns on simulated resistive crossbar arrays"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "in datacenter performance analysis of a tensor processing unit"
    - "analog cmos based resistive processing unit for deep neural network training"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "deep learning with limited numerical precision"
    - "deep speech scaling up end to end speech recognition"

Connected through hyperedge HE-3 (induced by source paper "geniex a generalized approach to emulating non ideality in memristive xbars using neural networks"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "x changr changing memristive crossbar mapping for mitigating line resistance induced accuracy degradation in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "x changr changing memristive crossbar mapping for mitigating line resistance induced accuracy degradation in deep neural networks"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "neuromorphic computing with multi memristive synapses"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "8t sram cell as a multi bit dot product engine for beyond von neumann computing"
    - "xcel ram accelerating binary neural networks in high throughput sram compute arrays"
    - "group scissor scaling neuromorphic computing design to large neural networks"
    - "trannsformer n eural n etwork transform ation for memristive crossbar based neuromorphic system design"
    - "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"

Connected through hyperedge HE-5 (induced by source paper "a photonic in memory computing primitive for spiking neural networks using phase change materials"):
  with members:
    - "toward fast neural computing using all photonic phase change spiking neurons"
    - "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "brain like associative learning using a nanoscale non volatile phase change synaptic device array"

Connected through hyperedge HE-6 (induced by source paper "defects mitigation in resistive crossbars for analog vector matrix multiplication"):
  with members:
    - "memristor based deep convolution neural network a case study"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "hitting the memory wall implications of the obvious"

Connected through hyperedge HE-7 (induced by source paper "8t sram cell as a multi bit dot product engine for beyond von neumann computing"):
  with members:
    - "x sram enabling in memory boolean computations in cmos static random access memories"
    - "the power of sparsity in convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "toward fast neural computing using all photonic phase change spiking neurons"):
  with members:
    - "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"

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)