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: "analog cmos based resistive processing unit for deep neural network training"
  Abstract: Recently we have shown that an architecture based on resistive processing unit (RPU) devices has potential to achieve significant acceleration in deep neural network (DNN) training compared to today's software-based DNN implementations running on CPU/GPU. However, currently available device candidates based on non-volatile memory technologies do not satisfy all the requirements to realize the RPU concept. Here, we propose an analog CMOS-based RPU design (CMOS RPU) which can store and process data locally and can be operated in a massively parallel manner. We analyze various properties of the CMOS RPU to evaluate the functionality and feasibility for acceleration of DNN training.

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 "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"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "in datacenter performance analysis of a tensor processing unit"
    - "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 "a back end cmos compatible ferroelectric field effect transistor for synaptic weights"):
  with members:
    - "neuromorphic computing with multi memristive synapses"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "a multi bit neuromorphic weight cell using ferroelectric fets suitable for soc integration"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"

Connected through hyperedge HE-4 (induced by source paper "learning in memristive neural network architectures using analog backpropagation circuits"):
  with members:
    - "neuromemristive circuits for edge computing a review"
    - "neuron inspired data encoding memristive multi level memory cell"
    - "hierarchical temporal memory using memristor networks a survey"
    - "binary weighted memristive analog deep neural network for near sensor edge processing"
    - "design of cmos memristor circuits for lstm architecture"
    - "lstm a search space odyssey"
    - "on chip face recognition system design with memristive hierarchical temporal memory"
    - "multi level memristive memory with resistive networks"

Connected through hyperedge HE-5 (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"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "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-6 (induced by source paper "streaming batch eigenupdates for hardware neural networks"):
  with members:
    - "training lstm networks with resistive cross point devices"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "the fast convergence of incremental pca"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "the noisy power method a meta algorithm with applications"

Connected through hyperedge HE-7 (induced by source paper "on chip learning in a conventional silicon mosfet based analog hardware neural network"):
  with members:
    - "a simple and efficient snn and its performance robustness evaluation method to enable hardware implementation"

Connected through hyperedge HE-8 (induced by source paper "training lstm networks with resistive cross point devices"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "a network of deep neural networks for distant speech recognition"
    - "dilated recurrent neural networks"
    - "dual path networks"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "exploring the limits of language modeling"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "google s neural machine translation system bridging the gap between human and 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.ET(Emerging Technologies)