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: "dnn neurosim v2 0 an end to end benchmarking framework for compute in memory accelerators for on chip training"
  Abstract: DNN+NeuroSim is an integrated framework to benchmark compute-in-memory (CIM) accelerators for deep neural networks, with hierarchical design options from device-level, to circuit-level and up to algorithm-level. A python wrapper is developed to interface NeuroSim with a popular machine learning platform: Pytorch, to support flexible network structures. The framework provides automatic algorithm-to-hardware mapping, and evaluates chip-level area, energy efficiency and throughput for training or inference, as well as training/inference accuracy with hardware constraints. Our prior work (DNN+NeuroSim V1.1) was developed to estimate the impact of reliability in synaptic devices, and analog-to-digital converter (ADC) quantization loss on the accuracy and hardware performance of inference engines. In this work, we further investigated the impact of the analog emerging non-volatile memory non-ideal device properties for on-chip training. By introducing the nonlinearity, asymmetry, device-to-device and cycle-to-cycle variation of weight update into the python wrapper, and peripheral circuits for error/weight gradient computation in NeuroSim core, we benchmarked CIM accelerators based on state-of-the-art SRAM and eNVM devices for VGG-8 on CIFAR-10 dataset, revealing the crucial specs of synaptic devices for on-chip training. The proposed DNN+NeuroSim V2.0 framework is available on GitHub.

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)