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: "n2n learning network to network compression via policy gradient reinforcement learning"
  Abstract: While bigger and deeper neural network architectures continue to advance the state-of-the-art for many computer vision tasks, real-world adoption of these networks is impeded by hardware and speed constraints. Conventional model compression methods attempt to address this problem by modifying the architecture manually or using pre-defined heuristics. Since the space of all reduced architectures is very large, modifying the architecture of a deep neural network in this way is a difficult task. In this paper, we tackle this issue by introducing a principled method for learning reduced network architectures in a data-driven way using reinforcement learning. Our approach takes a larger `teacher' network as input and outputs a compressed `student' network derived from the `teacher' network. In the first stage of our method, a recurrent policy network aggressively removes layers from the large `teacher' model. In the second stage, another recurrent policy network carefully reduces the size of each remaining layer. The resulting network is then evaluated to obtain a reward -- a score based on the accuracy and compression of the network. Our approach uses this reward signal with policy gradients to train the policies to find a locally optimal student network. Our experiments show that we can achieve compression rates of more than 10x for models such as ResNet-34 while maintaining similar performance to the input `teacher' network. We also present a valuable transfer learning result which shows that policies which are pre-trained on smaller `teacher' networks can be used to rapidly speed up training on larger `teacher' networks.

Connected through hyperedge HE-1 (induced by source paper "group sparsity the hinge between filter pruning and decomposition for network compression"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "3d appearance super resolution with deep learning"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "oicsr out in channel sparsity regularization for compact deep neural networks"
    - "gasl guided attention for sparsity learning in deep neural networks"
    - "importance estimation for neural network pruning"
    - "learning filter basis for convolutional neural network compression"
    - "similarity preserving knowledge distillation"

Connected through hyperedge HE-2 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "bigdl a distributed deep learning framework for big data"
    - "network decoupling from regular to depthwise separable convolutions"
    - "deep neural network compression with single and multiple level quantization"

Connected through hyperedge HE-3 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way"):
  with members:
    - "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "wide activation for efficient and accurate image super resolution"
    - "multi style generative network for real time transfer"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-4 (induced by source paper "automatic pruning for quantized neural networks"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "proxquant quantized neural networks via proximal operators"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "discrimination aware channel pruning for deep neural networks"
    - "relaxed quantization for discretized neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "skipnet learning dynamic routing in convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "pops policy pruning and shrinking for deep reinforcement learning"):
  with members:
    - "legr filter pruning via learned global ranking"
    - "structured pruning of neural networks with budget aware regularization"
    - "amc automl for model compression and acceleration on mobile devices"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "layer compensated pruning for resource constrained convolutional neural networks"
    - "deep learning in mobile and wireless networking a survey"
    - "deep reinforcement one shot learning for artificially intelligent classification systems"
    - "deep reinforcement learning an overview"

Connected through hyperedge HE-6 (induced by source paper "a privacy preserving dnn pruning and mobile acceleration framework"):
  with members:
    - "asymptotic soft filter pruning for deep convolutional neural networks"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "data free learning of student networks"
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "amc automl for model compression and acceleration on mobile devices"
    - "private model compression via knowledge distillation"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

Connected through hyperedge HE-7 (induced by source paper "ss auto a single shot automatic structured weight pruning framework of dnns with ultra high efficiency"):
  with members:
    - "non structured dnn weight pruning considered harmful"
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "amc automl for model compression and acceleration on mobile devices"
    - "discrimination aware channel pruning for deep neural networks"
    - "systematic weight pruning of dnns using alternating direction method of multipliers"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-8 (induced by source paper "residual knowledge distillation"):
  with members:
    - "esrgan enhanced super resolution generative adversarial networks"
    - "an embarrassingly simple approach for knowledge distillation"
    - "self supervised knowledge distillation using singular value decomposition"
    - "adversarial network compression"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"

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)