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: "rxnn a framework for evaluating deep neural networks on resistive crossbars"
  Abstract: Resistive crossbars have emerged as promising building blocks for realizing DNNs due to their ability to compactly and efficiently realize the dominant DNN computational kernel, viz., vector-matrix multiplication. However, a key challenge with resistive crossbars is that they suffer from a range of device and circuit level non-idealities such as interconnect parasitics, peripheral circuits, sneak paths, and process variations. These non-idealities can lead to errors in vector-matrix multiplication that eventually degrade the DNN's accuracy. There has been no study of the impact of non-idealities on the accuracy of large-scale DNNs, in part because existing device and circuit models are infeasible to use in application-level evaluation. In this work, we present a fast and accurate simulation framework to enable evaluation and re-training of large-scale DNNs on resistive crossbar based hardware fabrics. #R##N#We first characterize the impact of crossbar non-idealities on errors incurred in the realized vector-matrix multiplications and observe that the errors have significant data and hardware-instance dependence that should be considered. We propose a Fast Crossbar Model (FCM) to accurately capture the errors arising due to crossbar non-idealities while being four-to-five orders of magnitude faster than circuit simulation. Finally, we develop RxNN, a software framework to evaluate and re-train DNNs on resistive crossbar systems. RxNN is based on the popular Caffe machine learning framework, and we use it to evaluate a suite of large-scale DNNs developed for the ImageNet Challenge (ILSVRC). Our experiments reveal that resistive crossbar non-idealities can lead to significant accuracy degradations (9.6%-32%) for these large-scale DNNs. To the best of our knowledge, this work is the first quantitative evaluation of the accuracy of large-scale DNNs on resistive crossbar based hardware.

Connected through hyperedge HE-1 (induced by source paper "noisy machines understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation"):
  with members:
    - "zero shot knowledge transfer via adversarial belief matching"
    - "adversarially robust distillation"
    - "accurate deep neural network inference using computational phase change memory"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "neuromorphic computing with multi memristive synapses"
    - "mixed precision in memory computing"
    - "all optical machine learning using diffractive deep neural networks"
    - "model compression via distillation and quantization"

Connected through hyperedge HE-2 (induced by source paper "compiling neural networks for a computational memory accelerator"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "stripe tensor compilation via the nested polyhedral model"
    - "a fast analytical model of fully associative caches"
    - "automated derivation of parametric data movement lower bounds for affine programs"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "glow graph lowering compiler techniques for neural networks"
    - "a hardware software blueprint for flexible deep learning specialization"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"

Connected through hyperedge HE-3 (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"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic 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-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"
    - "8t sram cell as a multi bit dot product engine for beyond von neumann computing"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "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 "tim dnn ternary in memory accelerator for deep neural networks"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "pact parameterized clipping activation for quantized neural networks"
    - "wrpn wide reduced precision networks"
    - "ternary neural networks with fine grained quantization"
    - "computing in memory with spin transfer torque magnetic ram"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "effective quantization methods for recurrent neural networks"
    - "trained ternary quantization"

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)