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: "low precision batch normalized activations"
  Abstract: Artificial neural networks can be trained with relatively low-precision floating-point and fixed-point arithmetic, using between one and 16 bits. Previous works have focused on relatively wide-but-shallow, feed-forward networks. We introduce a quantization scheme that is compatible with training very deep neural networks. Quantizing the network activations in the middle of each batch-normalization module can greatly reduce the amount of memory and computational power needed, with little loss in accuracy.

Connected through hyperedge HE-1 (induced by source paper "constructing energy efficient mixed precision neural networks through principal component analysis for edge intelligence"):
  with members:
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "per tensor fixed point quantization of the back propagation algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "a low effort approach to structured cnn design using pca"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "hybrid binary networks optimizing for accuracy efficiency and memory"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "pact parameterized clipping activation for quantized neural networks"

Connected through hyperedge HE-2 (induced by source paper "pca driven hybrid network design for enabling intelligence at the edge"):
  with members:
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "per tensor fixed point quantization of the back propagation algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "a low effort approach to structured cnn design using pca"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "pact parameterized clipping activation for quantized neural networks"
    - "wrpn wide reduced precision networks"

Connected through hyperedge HE-3 (induced by source paper "wrpn wide reduced precision networks"):
  with members:
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "finn a framework for fast scalable binarized neural network inference"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ternary weight networks"
    - "wide residual networks"
    - "convolutional neural networks using logarithmic data representation"
    - "trained ternary quantization"

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)