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: "dynamic stripes exploiting the dynamic precision requirements of activation values in neural networks"
  Abstract: Stripes is a Deep Neural Network (DNN) accelerator that uses bit-serial computation to offer performance that is proportional to the fixed-point precision of the activation values. The fixed-point precisions are determined a priori using profiling and are selected at a per layer granularity. This paper presents Dynamic Stripes, an extension to Stripes that detects precision variance at runtime and at a finer granularity. This extra level of precision reduction increases performance by 41% over Stripes.

Connected through hyperedge HE-1 (induced by source paper "bitpruning learning bitlengths for aggressive and accurate quantization"):
  with members:
    - "differentiable quantization of deep neural networks"
    - "pact parameterized clipping activation for quantized neural networks"
    - "value aware quantization for training and inference of neural networks"
    - "dpred making typical activation values matter in deep learning computing"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep residual learning for image recognition"
    - "reduced precision strategies for bounded memory in deep neural nets"

Connected through hyperedge HE-2 (induced by source paper "dpred making typical activation and weight values matter in deep learning computing"):
  with members:
    - "value aware quantization for training and inference of neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning deep cnn denoiser prior for image restoration"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "tartan accelerating fully connected and convolutional layers in deep learning networks by exploiting numerical precision variability"

Connected through hyperedge HE-3 (induced by source paper "bit tactical exploiting ineffectual computations in convolutional neural networks which why and how"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "accelerating deep convolutional networks using low precision and sparsity"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "reduced precision strategies for bounded memory in deep neural nets"

Connected through hyperedge HE-4 (induced by source paper "laconic deep learning computing"):
  with members:
    - "dpred making typical activation values matter in deep learning computing"
    - "scnn an accelerator for compressed sparse convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "loom exploiting weight and activation precisions to accelerate convolutional neural networks"):
  with members:
    - "dpred making typical activation and weight values matter in deep learning computing"
    - "ssd single shot multibox detector"
    - "bit pragmatic deep neural network computing"
    - "eie efficient inference engine on compressed deep neural network"
    - "reduced precision strategies for bounded memory in deep neural nets"
    - "deep speech scaling up end to end speech recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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.NE(Neural and Evolutionary Computing)