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: "acceleration of deep neural network training with resistive cross point devices design considerations"
  Abstract: In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30,000X compared to state-of-the-art microprocessors while providing power efficiency of 84,000 GigaOps/s/W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things) sensors.

Connected through hyperedge HE-1 (induced by source paper "mixed precision deep learning based on computational memory"):
  with members:
    - "long short term memory networks in memristor crossbar arrays"
    - "neuromorphic computing with multi memristive synapses"
    - "mixed precision in memory computing"
    - "training lstm networks with resistive cross point devices"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "training and inference with integers in deep neural networks"
    - "an analysis of neural language modeling at multiple scales"
    - "an improved evaluation framework for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "hardware and software co optimization for the initialization failure of the reram based cross bar array"):
  with members:
    - "training lstm networks with resistive cross point devices"
    - "pact parameterized clipping activation for quantized neural networks"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "a back end cmos compatible ferroelectric field effect transistor for synaptic weights"):
  with members:
    - "neuromorphic computing with multi memristive synapses"
    - "analog cmos based resistive processing unit for deep neural network training"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "a multi bit neuromorphic weight cell using ferroelectric fets suitable for soc integration"

Connected through hyperedge HE-4 (induced by source paper "algorithm for training neural networks on resistive device arrays"):
  with members:
    - "training lstm networks with resistive cross point devices"
    - "a network of deep neural networks for distant speech recognition"
    - "dual path networks"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "exploring the limits of language modeling"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep residual learning for image recognition"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-5 (induced by source paper "long short term memory networks in memristor crossbar arrays"):
  with members:
    - "mixed precision in memory computing"
    - "approximate fpga based lstms under computation time constraints"
    - "chipmunk a systolically scalable 0 9 mm 2 3 08 gop s mw 1 2 mw accelerator for near sensor recurrent neural network inference"
    - "the microsoft 2017 conversational speech recognition system"
    - "learning to detect violent videos using convolutional long short term memory"
    - "data recombination for neural semantic parsing"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-6 (induced by source paper "training large scale anns on simulated resistive crossbar arrays"):
  with members:
    - "training lstm networks with resistive cross point devices"
    - "efficient convnets for analog arrays"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "zero shifting technique for deep neural network training on resistive cross point arrays"):
  with members:
    - "training large scale anns on simulated resistive crossbar arrays"
    - "deep neural network optimized to resistive memory with nonlinear current voltage characteristics"
    - "analog cmos based resistive processing unit for deep neural network training"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "deep residual learning for image recognition"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "caffe convolutional architecture for fast feature embedding"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-8 (induced by source paper "adaptive learning rule for hardware based deep neural networks using electronic synapse devices"):
  with members:
    - "lstm a search space odyssey"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "precise deep neural network computation on imprecise low power analog hardware"
    - "deep residual learning for image recognition"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "redesigning commercial floating gate memory for analog computing applications"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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.LG(Machine Learning)