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: "dark memory and accelerator rich system optimization in the dark silicon era"
  Abstract: Unlike traditional dark silicon works that attack the computing logic, this article puts a focus on the memory part, which dissipates most of the energy for memory-bound CPU applications. This article discusses the dark memory state and present Pareto curves for compute units, accelerators, and on-chip memory, and motivates the need for HW/SW codesign for parallelism and locality.  –Muhammad Shafique, Vienna University of Technology

Connected through hyperedge HE-1 (induced by source paper "campfire compressible regularization free structured sparse training for hardware accelerators"):
  with members:
    - "low memory neural network training a technical report"
    - "prunetrain gradual structured pruning from scratch for faster neural network training"
    - "full deep neural network training on a pruned weight budget"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "the state of sparsity in deep neural networks"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "network compression using correlation analysis of layer responses"

Connected through hyperedge HE-2 (induced by source paper "domain aware dynamic networks"):
  with members:
    - "test time training for out of distribution generalization"
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "multi source domain adaptation for semantic segmentation"
    - "condconv conditionally parameterized convolutions for efficient inference"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"

Connected through hyperedge HE-3 (induced by source paper "digital biologically plausible implementation of binarized neural networks with differential hafnium oxide resistive memory arrays"):
  with members:
    - "nvidia tensor core programmability performance precision"
    - "in memory and error immune differential rram implementation of binarized deep neural networks"
    - "towards accurate binary convolutional neural network"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "memory and information processing in neuromorphic systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-4 (induced by source paper "a case for superconducting accelerators"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-5 (induced by source paper "caterpillar coarse grain reconfigurable architecture for accelerating the training of deep neural networks"):
  with members:
    - "efficient processing of deep neural networks a tutorial and survey"
    - "eie efficient inference engine on compressed deep neural network"
    - "fpga based implementation of deep neural networks using on chip memory only"
    - "deep learning with limited numerical precision"
    - "how important is weight symmetry in backpropagation"
    - "very deep convolutional networks for large scale image recognition"
    - "deep big simple neural nets for handwritten digit recognition"

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)