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: "fine pruning joint fine tuning and compression of a convolutional network with bayesian optimization"
  Abstract: When approaching a novel visual recognition problem in a specialized image domain, a common strategy is to start with a pre-trained deep neural network and fine-tune it to the specialized domain. If the target domain covers a smaller visual space than the source domain used for pre-training (e.g. ImageNet), the fine-tuned network is likely to be over-parameterized. However, applying network pruning as a post-processing step to reduce the memory requirements has drawbacks: fine-tuning and pruning are performed independently; pruning parameters are set once and cannot adapt over time; and the highly parameterized nature of state-of-the-art pruning methods make it prohibitive to manually search the pruning parameter space for deep networks, leading to coarse approximations. We propose a principled method for jointly fine-tuning and compressing a pre-trained convolutional network that overcomes these limitations. Experiments on two specialized image domains (remote sensing images and describable textures) demonstrate the validity of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "on iterative neural network pruning reinitialization and the similarity of masks"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "on activation function coresets for network pruning"
    - "exploring randomly wired neural networks for image recognition"
    - "the state of sparsity in deep neural networks"
    - "how important is a neuron"
    - "understanding individual neuron importance using information theory"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "revisiting the importance of individual units in cnns via ablation"

Connected through hyperedge HE-2 (induced by source paper "domain adaptation regularization for spectral pruning"):
  with members:
    - "compression based bound for non compressed network unified generalization error analysis of large compressible deep neural network"
    - "accelerating deep unsupervised domain adaptation with transfer channel pruning"
    - "target aware network adaptation for efficient representation learning"
    - "network compression using correlation analysis of layer responses"
    - "domain adaptive deep network compression"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "pruning convolutional neural networks for resource efficient inference"

Connected through hyperedge HE-3 (induced by source paper "compressing weight updates for image artifacts removal neural networks"):
  with members:
    - "compressibility loss for neural network weights"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "deep convolution networks for compression artifacts reduction"
    - "u net convolutional networks for biomedical image segmentation"
    - "compression artifacts reduction by a deep convolutional network"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-4 (induced by source paper "fine pruning defending against backdooring attacks on deep neural networks"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "when does machine learning fail generalized transferability for evasion and poisoning attacks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "towards poisoning of deep learning algorithms with back gradient optimization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "adversarial example defenses ensembles of weak defenses are not strong"

Connected through hyperedge HE-5 (induced by source paper "a systematic dnn weight pruning framework using alternating direction method of multipliers"):
  with members:
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "thinet a filter level pruning method for deep neural network compression"
    - "alternating direction method of multipliers for sparse convolutional neural networks"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "nest a neural network synthesis tool based on a grow and prune paradigm"
    - "learning structured sparsity in deep neural networks"
    - "dynamic network surgery for efficient dnns"
    - "designing energy efficient convolutional neural networks using energy aware pruning"

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)