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: "data free parameter pruning for deep neural networks"
  Abstract: Deep Neural nets (NNs) with millions of parameters are at the heart of many state-of-the-art computer vision systems today. However, recent works have shown that much smaller models can achieve similar levels of performance. In this work, we address the problem of pruning parameters in a trained NN model. Instead of removing individual weights one at a time as done in previous works, we remove one neuron at a time. We show how similar neurons are redundant, and propose a systematic way to remove them. Our experiments in pruning the densely connected layers show that we can remove upto 85\% of the total parameters in an MNIST-trained network, and about 35\% for AlexNet without significantly affecting performance. Our method can be applied on top of most networks with a fully connected layer to give a smaller network.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving learning via deep net pruning"):
  with members:
    - "deep leakage from gradients"
    - "stronger l2 l2 compressed sensing without iterating"
    - "inverting deep generative models one layer at a time"
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "the state of sparsity in deep neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "federated learning strategies for improving communication efficiency"
    - "split learning for health distributed deep learning without sharing raw patient data"

Connected through hyperedge HE-2 (induced by source paper "generative low bitwidth data free quantization"):
  with members:
    - "zeroq a novel zero shot quantization framework"
    - "data free quantization through weight equalization and bias correction"
    - "effective training of convolutional neural networks with low bitwidth weights and activations"
    - "zero shot knowledge transfer via adversarial belief matching"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "data free learning of student networks"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "zero shot knowledge distillation in deep networks"

Connected through hyperedge HE-3 (induced by source paper "identifying critical neurons in ann architectures using mixed integer programming"):
  with members:
    - "lossless compression of deep neural networks"
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "pruning at a glance global neural pruning for model compression"
    - "a benchmark for interpretability methods in deep neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "understanding individual neuron importance using information theory"
    - "pruning deep neural networks using partial least squares"
    - "towards understanding the role of over parametrization in generalization of neural networks"

Connected through hyperedge HE-4 (induced by source paper "proving the lottery ticket hypothesis pruning is all you need"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "effect of activation functions on the training of overparametrized neural nets"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "what s hidden in a randomly weighted neural network"
    - "on the power and limitations of random features for understanding neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "complexity of training relu neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"

Connected through hyperedge HE-5 (induced by source paper "pcnn pattern based fine grained regular pruning towards optimizing cnn accelerators"):
  with members:
    - "non structured dnn weight pruning considered harmful"
    - "req yolo a resource aware efficient quantization framework for object detection on fpgas"
    - "learning to prune filters in convolutional neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "efficient hardware realization of convolutional neural networks using intra kernel regular pruning"
    - "learning efficient convolutional networks through network slimming"
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-6 (induced by source paper "a note on latency variability of deep neural networks for mobile inference"):
  with members:
    - "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning"
    - "characterizing the deep neural networks inference performance of mobile applications"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "pruning filters for efficient convnets"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-7 (induced by source paper "depth wise decomposition for accelerating separable convolutions in efficient convolutional neural networks"):
  with members:
    - "mobinet a mobile binary network for image classification"
    - "pack and detect fast object detection in videos using region of interest packing"
    - "feature selective anchor free module for single shot object detection"
    - "prediction tracking segmentation"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-8 (induced by source paper "towards compact convnets via structure sparsity regularized filter pruning"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "attention based guided structured sparsity of deep neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

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)