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: "adjustable bounded rectifiers towards deep binary representations"
  Abstract: Binary representation is desirable for its memory efficiency, computation speed and robustness. In this paper, we propose adjustable bounded rectifiers to learn binary representations for deep neural networks. While hard constraining representations across layers to be binary makes training unreasonably difficult, we softly encourage activations to diverge from real values to binary by approximating step functions. Our final representation is completely binary. We test our approach on MNIST, CIFAR10, and ILSVRC2012 dataset, and systematically study the training dynamics of the binarization process. Our approach can binarize the last layer representation without loss of performance and binarize all the layers with reasonably small degradations. The memory space that it saves may allow more sophisticated models to be deployed, thus compensating the loss. To the best of our knowledge, this is the first work to report results on current deep network architectures using complete binary middle representations. Given the learned representations, we find that the firing or inhibition of a binary neuron is usually associated with a meaningful interpretation across different classes. This suggests that the semantic structure of a neural network may be manifested through a guided binarization process.

Connected through hyperedge HE-1 (induced by source paper "evidence for the size principle in semantic and perceptual domains"):
  with members:
    - "adapting deep network features to capture psychological representations"

Connected through hyperedge HE-2 (induced by source paper "structured convolution matrices for energy efficient deep learning"):
  with members:
    - "noisy activation functions"
    - "eie efficient inference engine on compressed deep neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "acdc a structured efficient linear layer"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "convolutional networks for fast energy efficient neuromorphic computing"):
  with members:
    - "conversion of artificial recurrent neural networks to spiking neural networks for low power neuromorphic hardware"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "learning both weights and connections for efficient neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "gibbs sampling with low power spiking digital neurons"
    - "striving for simplicity the all convolutional net"
    - "matconvnet convolutional neural networks for matlab"
    - "flattened convolutional neural networks for feedforward acceleration"

Connected through hyperedge HE-4 (induced by source paper "dehazenet an end to end system for single image haze removal"):
  with members:
    - "robust face recognition via multimodal deep face representation"
    - "pcanet a simple deep learning baseline for image classification"
    - "going deeper with convolutions"
    - "image super resolution using deep convolutional networks"
    - "imagenet large scale visual recognition challenge"
    - "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.LG(Machine Learning)