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: "compressing convolutional neural networks"
  Abstract: Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers. We present a novel network architecture, Frequency-Sensitive Hashed Nets (FreshNets), which exploits inherent redundancy in both convolutional layers and fully-connected layers of a deep learning model, leading to dramatic savings in memory and storage consumption. Based on the key observation that the weights of learned convolutional filters are typically smooth and low-frequency, we first convert filter weights to the frequency domain with a discrete cosine transform (DCT) and use a low-cost hash function to randomly group frequency parameters into hash buckets. All parameters assigned the same hash bucket share a single value learned with standard back-propagation. To further reduce model size we allocate fewer hash buckets to high-frequency components, which are generally less important. We evaluate FreshNets on eight data sets, and show that it leads to drastically better compressed performance than several relevant baselines.

Connected through hyperedge HE-1 (induced by source paper "co evolutionary compression for unpaired image translation"):
  with members:
    - "amalgamating knowledge towards comprehensive classification"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "large scale evolution of image classifiers"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "improving efficiency in convolutional neural networks with multilinear filters"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "balanced quantization an effective and efficient approach to quantized neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tensor contraction layers for parsimonious deep nets"
    - "tensor representation in high frequency financial data for price change prediction"
    - "learning structured sparsity in deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "hardware oriented approximation of convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "bitnet bit regularized deep neural networks"):
  with members:
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "mixed low precision deep learning inference using dynamic fixed point"
    - "alternating direction method of multipliers for sparse convolutional neural networks"
    - "fixed point optimization of deep neural networks with adaptive step size retraining"
    - "two bit networks for deep learning on resource constrained embedded devices"
    - "towards the limit of network quantization"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "learning bag of features pooling for deep convolutional neural networks"):
  with members:
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "quantized convolutional neural networks for mobile devices"
    - "deep residual learning for image recognition"
    - "learning to answer questions from image using convolutional neural network"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-5 (induced by source paper "asp vision optically computing the first layer of convolutional neural networks using angle sensitive pixels"):
  with members:
    - "convolutional sparse coding for high dynamic range imaging"
    - "deep fully connected networks for video compressive sensing"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "neural networks with few multiplications"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "reconstruction free action inference from compressive imagers"
    - "towards trainable media using waves for neural network style training"

Connected through hyperedge HE-6 (induced by source paper "sketching and neural networks"):
  with members:
    - "learning both weights and connections for efficient neural networks"
    - "a deep learning approach to structured signal recovery"
    - "memory bounded deep convolutional networks"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "do deep nets really need to be deep"
    - "low rank approximation and regression in input sparsity time"
    - "lower bounds for sparse recovery"
    - "a sparse johnson lindenstrauss transform"

Connected through hyperedge HE-7 (induced by source paper "in teacher we trust learning compressed models for pedestrian detection"):
  with members:
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ten years of pedestrian detection what have we learned"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-8 (induced by source paper "quantized convolutional neural networks for mobile devices"):
  with members:
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "data free parameter pruning for deep neural networks"
    - "fast convnets using group wise brain damage"
    - "fast r cnn"
    - "compressing neural networks with the hashing trick"
    - "accelerating very deep convolutional networks for classification and detection"
    - "faster r cnn towards real time object detection with region proposal 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.LG(Machine Learning)