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: "learning a wavelet like auto encoder to accelerate deep neural networks"
  Abstract: Accelerating deep neural networks (DNNs) has been attracting increasing attention as it can benefit a wide range of applications, e.g., enabling mobile systems with limited computing resources to own powerful visual recognition ability. A practical strategy to this goal usually relies on a two-stage process: operating on the trained DNNs (e.g., approximating the convolutional filters with tensor decomposition) and fine-tuning the amended network, leading to difficulty in balancing the trade-off between acceleration and maintaining recognition performance. In this work, aiming at a general and comprehensive way for neural network acceleration, we develop a Wavelet-like Auto-Encoder (WAE) that decomposes the original input image into two low-resolution channels (sub-images) and incorporate the WAE into the classification neural networks for joint training. The two decomposed channels, in particular, are encoded to carry the low-frequency information (e.g., image profiles) and high-frequency (e.g., image details or noises), respectively, and enable reconstructing the original input image through the decoding process. Then, we feed the low-frequency channel into a standard classification network such as VGG or ResNet and employ a very lightweight network to fuse with the high-frequency channel to obtain the classification result. Compared to existing DNN acceleration solutions, our framework has the following advantages: i) it is tolerant to any existing convolutional neural networks for classification without amending their structures; ii) the WAE provides an interpretable way to preserve the main components of the input image for classification.

Connected through hyperedge HE-1 (induced by source paper "a network pruning network approach to deep model compression"):
  with members:
    - "cooperative initialization based deep neural network training"
    - "accuracy booster performance boosting using feature map re calibration"
    - "cpwc contextual point wise convolution for object recognition"
    - "play and prune adaptive filter pruning for deep model compression"
    - "stability based filter pruning for accelerating deep cnns"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "amc automl for model compression and acceleration on mobile devices"
    - "leveraging filter correlations for deep model compression"

Connected through hyperedge HE-2 (induced by source paper "thumbnet one thumbnail image contains all you need for recognition"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "demystifying neural style transfer"
    - "thinet a filter level pruning method for deep neural network compression"
    - "a deep convolutional auto encoder with pooling unpooling layers in caffe"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "pruning filters for efficient convnets"
    - "tensorflow a system for large scale machine learning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-3 (induced by source paper "leveraging filter correlations for deep model compression"):
  with members:
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "cpwc contextual point wise convolution for object recognition"
    - "play and prune adaptive filter pruning for deep model compression"
    - "stability based filter pruning for accelerating deep cnns"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "amc automl for model compression and acceleration on mobile devices"
    - "model compression via distillation and quantization"

Connected through hyperedge HE-4 (induced by source paper "elastic improving cnns with dynamic scaling policies"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "deep layer aggregation"
    - "dilated residual networks"
    - "squeeze and excitation networks"
    - "adaptive feeding achieving fast and accurate detections by adaptively combining object detectors"
    - "condensenet an efficient densenet using learned group convolutions"
    - "learning spatial regularization with image level supervisions for multi label image classification"

Connected through hyperedge HE-5 (induced by source paper "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "knowledge embedded representation learning for fine grained image recognition"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "diversified visual attention networks for fine grained object classification"
    - "fine grained image classification via combining vision and language"
    - "low rank bilinear pooling for fine grained classification"
    - "disc deep image saliency computing via progressive representation learning"

Connected through hyperedge HE-6 (induced by source paper "paraphrasing complex network network compression via factor transfer"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "energy based generative adversarial network"
    - "wide residual networks"
    - "neural architecture search with reinforcement learning"
    - "designing neural network architectures using reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "learning deep similarity models with focus ranking for fabric image retrieval"):
  with members:
    - "multi label image recognition by recurrently discovering attentional regions"
    - "disc deep image saliency computing via progressive representation learning"
    - "deep ranking for person re identification via joint representation learning"
    - "a deep structured model with radius margin bound for 3d human activity recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "fast r cnn"
    - "deep metric learning via lifted structured feature embedding"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"

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.CV(Computer Vision and Pattern Recognition)