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: "revisiting the importance of individual units in cnns via ablation"
  Abstract: We revisit the importance of the individual units in Convolutional Neural Networks (CNNs) for visual recognition. By conducting unit ablation experiments on CNNs trained on large scale image datasets, we demonstrate that, though ablating any individual unit does not hurt overall classification accuracy, it does lead to significant damage on the accuracy of specific classes. This result shows that an individual unit is specialized to encode information relevant to a subset of classes. We compute the correlation between the accuracy drop under unit ablation and various attributes of an individual unit such as class selectivity and weight L1 norm. We confirm that unit attributes such as class selectivity are a poor predictor for impact on overall accuracy as found previously in recent work \cite{morcos2018importance}. However, our results show that class selectivity along with other attributes are good predictors of the importance of one unit to individual classes. We evaluate the impact of random rotation, batch normalization, and dropout to the importance of units to specific classes. Our results show that units with high selectivity play an important role in network classification power at the individual class level. Understanding and interpreting the behavior of these units is necessary and meaningful.

Connected through hyperedge HE-1 (induced by source paper "hrank filter pruning using high rank feature map"):
  with members:
    - "filter sketch for network pruning"
    - "channel pruning via automatic structure search"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "recent advances in efficient computation of deep convolutional neural networks"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "data driven sparse structure selection for deep neural networks"

Connected through hyperedge HE-2 (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"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-3 (induced by source paper "cheetah mixed low precision hardware software co design framework for dnns on the edge"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "mixed precision training with 8 bit floating point"
    - "performance efficiency trade off of low precision numerical formats in deep neural networks"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "deep positron a deep neural network using the posit number system"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "rethinking floating point for deep learning"

Connected through hyperedge HE-4 (induced by source paper "understanding deep networks via extremal perturbations and smooth masks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "constrained cnn losses for weakly supervised segmentation"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "rise randomized input sampling for explanation of black box models"
    - "sanity checks for saliency maps"
    - "network dissection quantifying interpretability of deep visual representations"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-5 (induced by source paper "functionality oriented convolutional filter pruning"):
  with members:
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "scsp spectral clustering filter pruning with soft self adaption manners"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "rethinking the value of network pruning"
    - "nisp pruning networks using neuron importance score propagation"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "a benchmark for interpretability methods in deep neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "do better imagenet models transfer better"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "rise randomized input sampling for explanation of black box models"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-7 (induced by source paper "deep learning training on the edge with low precision posits"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "template based posit multiplication for training and inferring in neural networks"
    - "mixed precision training with 8 bit floating point"
    - "performance efficiency trade off of low precision numerical formats in deep neural networks"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "deep positron a deep neural network using the posit number system"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "rethinking floating point for deep learning"

Connected through hyperedge HE-8 (induced by source paper "normlime a new feature importance metric for explaining deep neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "global explanations of neural networks mapping the landscape of predictions"
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "sanity checks for saliency maps"
    - "evaluating feature importance estimates"
    - "smoothgrad removing noise by adding noise"

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)