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: "an entropy based pruning method for cnn compression"
  Abstract: This paper aims to simultaneously accelerate and compress off-the-shelf CNN models via filter pruning strategy. The importance of each filter is evaluated by the proposed entropy-based method first. Then several unimportant filters are discarded to get a smaller CNN model. Finally, fine-tuning is adopted to recover its generalization ability which is damaged during filter pruning. Our method can reduce the size of intermediate activations, which would dominate most memory footprint during model training stage but is less concerned in previous compression methods. Experiments on the ILSVRC-12 benchmark demonstrate the effectiveness of our method. Compared with previous filter importance evaluation criteria, our entropy-based method obtains better performance. We achieve 3.3x speed-up and 16.64x compression on VGG-16, 1.54x acceleration and 1.47x compression on ResNet-50, both with about 1% top-5 accuracy decrease.

Connected through hyperedge HE-1 (induced by source paper "progressive gradient pruning for classification detection and domainadaptation"):
  with members:
    - "importance estimation for neural network pruning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "structured pruning of neural networks with budget aware regularization"
    - "yolov3 an incremental improvement"
    - "sbnet sparse blocks network for fast inference"
    - "discrimination aware channel pruning for deep neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

Connected through hyperedge HE-2 (induced by source paper "analysing deep reinforcement learning agents trained with domain randomisation"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "emergent systematic generalization in a situated agent"
    - "learning dexterous in hand manipulation"
    - "an introduction to deep reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "an improved trade off between accuracy and complexity with progressive gradient pruning"):
  with members:
    - "yolov3 an incremental improvement"
    - "private model compression via knowledge distillation"
    - "sbnet sparse blocks network for fast inference"
    - "discrimination aware channel pruning for deep neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "deep mutual learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "darc differentiable architecture compression"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "discrimination aware channel pruning for deep neural networks"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "shift a zero flop zero parameter alternative to spatial convolutions"

Connected through hyperedge HE-5 (induced by source paper "a survey of pruning methods for efficient person re identification across domains"):
  with members:
    - "play and prune adaptive filter pruning for deep model compression"
    - "building efficient convnets using redundant feature pruning"
    - "progressive deep neural networks acceleration via soft filter pruning"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "channel pruning for accelerating very deep neural networks"
    - "gated siamese convolutional neural network architecture for human re identification"

Connected through hyperedge HE-6 (induced by source paper "separable convolutional eigen filters scef building efficient cnns using redundancy analysis"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "network compression using correlation analysis of layer responses"
    - "extreme network compression via filter group approximation"
    - "nisp pruning networks using neuron importance score propagation"
    - "thinet a filter level pruning method for deep neural network compression"
    - "coordinating filters for faster deep neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "overcoming catastrophic forgetting by neuron level plasticity control"):
  with members:
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "sena cnn overcoming catastrophic forgetting in convolutional neural networks by selective network augmentation"
    - "deep generative dual memory network for continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "memory aware synapses learning what not to forget"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-8 (induced by source paper "play and prune adaptive filter pruning for deep model compression"):
  with members:
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "nisp pruning networks using neuron importance score propagation"
    - "thinet a filter level pruning method for deep neural network compression"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "pruning convolutional neural networks for resource efficient inference"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "feature pyramid networks for object detection"

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)