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: "2pfpce two phase filter pruning based on conditional entropy"
  Abstract: Deep Convolutional Neural Networks~(CNNs) offer remarkable performance of classifications and regressions in many high-dimensional problems and have been widely utilized in real-word cognitive applications. However, high computational cost of CNNs greatly hinder their deployment in resource-constrained applications, real-time systems and edge computing platforms. To overcome this challenge, we propose a novel filter-pruning framework, two-phase filter pruning based on conditional entropy, namely \textit{2PFPCE}, to compress the CNN models and reduce the inference time with marginal performance degradation. In our proposed method, we formulate filter pruning process as an optimization problem and propose a novel filter selection criteria measured by conditional entropy. Based on the assumption that the representation of neurons shall be evenly distributed, we also develop a maximum-entropy filter freeze technique that can reduce over fitting. Two filter pruning strategies -- global and layer-wise strategies, are compared. Our experiment result shows that combining these two strategies can achieve a higher neural network compression ratio than applying only one of them under the same accuracy drop threshold. Two-phase pruning, that is, combining both global and layer-wise strategies, achieves 10 X FLOPs reduction and 46% inference time reduction on VGG-16, with 2% accuracy drop.

Connected through hyperedge HE-1 (induced by source paper "an image enhancing pattern based sparsity for real time inference on mobile devices"):
  with members:
    - "autoslim towards one shot architecture search for channel numbers"
    - "deepcache principled cache for mobile deep vision"
    - "amc automl for model compression and acceleration on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "discrimination aware channel pruning for deep neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "rethinking the value of network pruning"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-2 (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"
    - "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"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"

Connected through hyperedge HE-3 (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"
    - "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"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-4 (induced by source paper "rtmobile beyond real time mobile acceleration of rnns for speech recognition"):
  with members:
    - "deepsz a novel framework to compress deep neural networks by using error bounded lossy compression"
    - "protecting neural networks with hierarchical random switching towards better robustness accuracy trade off for stochastic defenses"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "systematic weight pruning of dnns using alternating direction method of multipliers"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "e rnn design optimization for efficient recurrent neural networks in fpgas"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "blk rew a unified block based dnn pruning framework using reweighted regularization method"):
  with members:
    - "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"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "e rnn design optimization for efficient recurrent neural networks in fpgas"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-6 (induced by source paper "non structured dnn weight pruning is it beneficial in any platform"):
  with members:
    - "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"
    - "adversarial robustness vs model compression or both"
    - "hypar towards hybrid parallelism for deep learning accelerator array"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "morph flexible acceleration for 3d cnn based video understanding"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"

Connected through hyperedge HE-7 (induced by source paper "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "making convolutional networks shift invariant again"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "deepcache principled cache for mobile deep vision"
    - "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"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"

Connected through hyperedge HE-8 (induced by source paper "resnet can be pruned 60x introducing network purification and unused path removal p rm after weight pruning"):
  with members:
    - "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"
    - "structured weight matrices based hardware accelerators in deep neural networks fpgas and asics"
    - "amc automl for model compression and acceleration on mobile devices"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "learning structured sparsity in deep neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)