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: "training binary multilayer neural networks for image classification using expectation backpropagation"
  Abstract: Compared to Multilayer Neural Networks with real weights, Binary Multilayer Neural Networks (BMNNs) can be implemented more efficiently on dedicated hardware. BMNNs have been demonstrated to be effective on binary classification tasks with Expectation BackPropagation (EBP) algorithm on high dimensional text datasets. In this paper, we investigate the capability of BMNNs using the EBP algorithm on multiclass image classification tasks. The performances of binary neural networks with multiple hidden layers and different numbers of hidden units are examined on MNIST. We also explore the effectiveness of image spatial filters and the dropout technique in BMNNs. Experimental results on MNIST dataset show that EBP can obtain 2.12% test error with binary weights and 1.66% test error with real weights, which is comparable to the results of standard BackPropagation algorithm on fully connected MNNs.

Connected through hyperedge HE-1 (induced by source paper "recent advances in efficient computation of deep convolutional neural networks"):
  with members:
    - "from hashing to cnns training binaryweight networks via hashing"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning efficient convolutional networks through network slimming"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-2 (induced by source paper "extremely low bit neural network squeeze the last bit out with admm"):
  with members:
    - "deep learning with low precision by half wave gaussian quantization"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "binarized neural networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ternary weight networks"

Connected through hyperedge HE-3 (induced by source paper "performance guaranteed network acceleration via high order residual quantization"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "bitwise neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "data free parameter pruning for deep neural networks"
    - "adam a method for stochastic optimization"
    - "exploiting linear structure within convolutional networks for efficient evaluation"

Connected through hyperedge HE-4 (induced by source paper "a gpu outperforming fpga accelerator architecture for binary convolutional neural networks"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "bitwise neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "yodann an ultra low power convolutional neural network accelerator based on binary weights"
    - "resiliency of deep neural networks under quantization"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-5 (induced by source paper "training quantized nets a deeper understanding"):
  with members:
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ternary weight networks"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "wide residual networks"
    - "convolutional neural networks using logarithmic data representation"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-6 (induced by source paper "low precision neural networks using subband decomposition"):
  with members:
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "learning both weights and connections for efficient neural networks"
    - "deep learning with limited numerical precision"
    - "compressing deep convolutional networks using vector quantization"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-7 (induced by source paper "quantized neural networks training neural networks with low precision weights and activations"):
  with members:
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "hardware oriented approximation of convolutional neural networks"
    - "yodann an architecture for ultra low power binary weight cnn acceleration"
    - "bitwise neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "recurrent neural networks with limited numerical precision"
    - "yodann an ultra low power convolutional neural network accelerator based on binary weights"

Connected through hyperedge HE-8 (induced by source paper "fixed point factorized networks"):
  with members:
    - "binarized neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "imagenet pre trained models with batch normalization"
    - "bitwise neural networks"
    - "convolutional neural networks using logarithmic data representation"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "fixed point quantization of deep convolutional networks"
    - "learning both weights and connections for efficient neural networks"

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)