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: "x changr changing memristive crossbar mapping for mitigating line resistance induced accuracy degradation in deep neural networks"
  Abstract: There is widespread interest in emerging technologies, especially resistive crossbars for accelerating Deep Neural Networks (DNNs). Resistive crossbars offer a highly-parallel and efficient matrix-vector-multiplication (MVM) operation. MVM being the most dominant operation in DNNs makes crossbars ideally suited. However, various sources of device and circuit non-idealities lead to errors in the MVM output, thereby reducing DNN accuracy. Towards that end, we propose crossbar re-mapping strategies to mitigate line-resistance induced accuracy degradation in DNNs, without having to re-train the learned weights, unlike most prior works. Line-resistances degrade the voltage levels along the crossbar columns, thereby inducing more errors at the columns away from the drivers. We rank the DNN weights and kernels based on a sensitivity analysis, and re-arrange the columns such that the most sensitive kernels are mapped closer to the drivers, thereby minimizing the impact of errors on the overall accuracy. We propose two algorithms $-$ static remapping strategy (SRS) and dynamic remapping strategy (DRS), to optimize the crossbar re-arrangement of a pre-trained DNN. We demonstrate the benefits of our approach on a standard VGG16 network trained using CIFAR10 dataset. Our results show that SRS and DRS limit the accuracy degradation to 2.9\% and 2.1\%, respectively, compared to a 5.6\% drop from an as it is mapping of weights and kernels to crossbars. We believe this work brings an additional aspect for optimization, which can be used in tandem with existing mitigation techniques, such as in-situ compensation, technology aware training and re-training approaches, to enhance system performance.

Connected through hyperedge HE-1 (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"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"

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)