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: "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"
  Abstract: Neural networks are an increasingly attractive algorithm for natural language processing and pattern recognition. Deep networks with >50 M parameters are made possible by modern graphics processing unit clusters operating at    $270\times $    energy and    $540\times $    latency advantage over a similar block utilizing only digital ReRAM and takes only 11 fJ per multiply and accumulate. Compared with an SRAM-based accelerator, the energy is    $430\times $    better and latency is    $34\times $    better. Although training accuracy is degraded in the analog accelerator, several options to improve this are presented. The possible gains over a similar digital-only version of this accelerator block suggest that continued optimization of analog resistive memories is valuable. This detailed circuit and device analysis of a training accelerator may serve as a foundation for further architecture-level studies.

Connected through hyperedge HE-1 (induced by source paper "a supervised learning algorithm for multilayer spiking neural networks based on temporal coding toward energy efficient vlsi processor design"):
  with members:
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "deep learning in spiking neural networks"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"
    - "an energy efficient time domain analog vlsi neural network processor based on a pulse width modulation approach"
    - "supervised learning in spiking neural networks with phase change memory synapses"
    - "a time domain analog weighted sum calculation model for extremely low power vlsi implementation of multi layer neural networks"
    - "long short term memory and learning to learn in networks of spiking neurons"

Connected through hyperedge HE-2 (induced by source paper "plasticity enhanced domain wall mtj neural networks for energy efficient online learning"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "an online learning algorithm for neuromorphic hardware implementation"

Connected through hyperedge HE-3 (induced by source paper "benchmarking physical performance of neural inference circuits"):
  with members:
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "large scale neuromorphic spiking array processors a quest to mimic the brain"
    - "benchmarking inverse rashba edelstein magnetoelectric devices for neuromorphic computing"
    - "benchmarking keyword spotting efficiency on neuromorphic hardware"
    - "an analysis of deep neural network models for practical applications"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "origami a 803 gop s w convolutional network accelerator"

Connected through hyperedge HE-4 (induced by source paper "panther a programmable architecture for neural network training harnessing energy efficient reram"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "a study of bfloat16 for deep learning training"
    - "training high performance and large scale deep neural networks with full 8 bit integers"
    - "training and inference with integers in deep neural networks"
    - "training deep neural networks with 8 bit floating point numbers"
    - "revisiting small batch training for deep neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mixed precision training"

Connected through hyperedge HE-5 (induced by source paper "harnessing intrinsic noise in memristor hopfield neural networks for combinatorial optimization"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "mixed precision in memory computing"
    - "fault tolerant dot product engines"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "part detector discovery in deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "energy efficient time domain vector by matrix multiplier for neurocomputing and beyond"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "temperature insensitive analog vector by matrix multiplier based on 55 nm nor flash memory cells"
    - "sub 1 us sub 20 nj pattern classification in a mixed signal circuit based on embedded 180 nm floating gate memory cell arrays"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "precise deep neural network computation on imprecise low power analog hardware"
    - "jpeg image compression using discrete cosine transform a survey"

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.AR(Hardware Architecture)