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: "a novel channel pruning method for deep neural network compression"
  Abstract: In recent years, deep neural networks have achieved great success in the field of computer vision. However, it is still a big challenge to deploy these deep models on resource-constrained embedded devices such as mobile robots, smart phones and so on. Therefore, network compression for such platforms is a reasonable solution to reduce memory consumption and computation complexity. In this paper, a novel channel pruning method based on genetic algorithm is proposed to compress very deep Convolution Neural Networks (CNNs). Firstly, a pre-trained CNN model is pruned layer by layer according to the sensitivity of each layer. After that, the pruned model is fine-tuned based on knowledge distillation framework. These two improvements significantly decrease the model redundancy with less accuracy drop. Channel selection is a combinatorial optimization problem that has exponential solution space. In order to accelerate the selection process, the proposed method formulates it as a search problem, which can be solved efficiently by genetic algorithm. Meanwhile, a two-step approximation fitness function is designed to further improve the efficiency of genetic process. The proposed method has been verified on three benchmark datasets with two popular CNN models: VGGNet and ResNet. On the CIFAR-100 and ImageNet datasets, our approach outperforms several state-of-the-art methods. On the CIFAR-10 and SVHN datasets, the pruned VGGNet achieves better performance than the original model with 8 times parameters compression and 3 times FLOPs reduction.

Connected through hyperedge HE-1 (induced by source paper "deep elastic networks with model selection for multi task learning"):
  with members:
    - "deep virtual networks for memory efficient inference of multiple tasks"
    - "multi task zipping via layer wise neuron sharing"
    - "maskconnect connectivity learning by gradient descent"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "focal loss for dense object detection"
    - "blockdrop dynamic inference paths in residual networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "accelerating deep unsupervised domain adaptation with transfer channel pruning"):
  with members:
    - "finding answers from the word of god domain adaptation for neural networks in biblical question answering"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "balanced distribution adaptation for transfer learning"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "stratified transfer learning for cross domain activity recognition"
    - "adversarial discriminative domain adaptation"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-3 (induced by source paper "face recognition a novel multi level taxonomy based survey"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep face recognition a survey"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "ring loss convex feature normalization for face recognition"
    - "a double deep spatio angular learning framework for light field based face recognition"
    - "face recognition via centralized coordinate learning"
    - "nonlinear 3d face morphable model"

Connected through hyperedge HE-4 (induced by source paper "ensyth a pruning approach to synthesis of deep learning ensembles"):
  with members:
    - "fast convex pruning of deep neural networks"
    - "deep mutual learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "channel pruning for accelerating very deep neural networks"
    - "xception deep learning with depthwise separable convolutions"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-5 (induced by source paper "ascai adaptive sampling for acquiring compact ai"):
  with members:
    - "codex bit flexible encoding for streaming based fpga acceleration of dnns"
    - "amc automl for model compression and acceleration on mobile devices"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "data driven sparse structure selection for deep neural networks"
    - "large scale evolution of image classifiers"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "batch normalization sampling"):
  with members:
    - "l1 norm batch normalization for efficient training of deep neural networks"
    - "scalable methods for 8 bit training of neural networks"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "group normalization"
    - "structurally sparsified backward propagation for faster long short term memory training"
    - "understanding batch normalization"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "imagenet training in minutes"

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)