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: "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
  Abstract: Recent advances in deep learning motivate the use of deep neutral networks in sensing applications, but their excessive resource needs on constrained embedded devices remain an important impediment. A recently explored solution space lies in compressing (approximating or simplifying) deep neural networks in some manner before use on the device. We propose a new compression solution, called DeepIoT, that makes two key contributions in that space. First, unlike current solutions geared for compressing specific types of neural networks, DeepIoT presents a unified approach that compresses all commonly used deep learning structures for sensing applications, including fully-connected, convolutional, and recurrent neural networks, as well as their combinations. Second, unlike solutions that either sparsify weight matrices or assume linear structure within weight matrices, DeepIoT compresses neural network structures into smaller dense matrices by finding the minimum number of non-redundant hidden elements, such as filters and dimensions required by each layer, while keeping the performance of sensing applications the same. Importantly, it does so using an approach that obtains a global view of parameter redundancies, which is shown to produce superior compression. We conduct experiments with five different sensing-related tasks on Intel Edison devices. DeepIoT outperforms all compared baseline algorithms with respect to execution time and energy consumption by a significant margin. It reduces the size of deep neural networks by 90% to 98.9%. It is thus able to shorten execution time by 71.4% to 94.5%, and decrease energy consumption by 72.2% to 95.7%. These improvements are achieved without loss of accuracy. The results underscore the potential of DeepIoT for advancing the exploitation of deep neural networks on resource-constrained embedded devices.

Connected through hyperedge HE-1 (induced by source paper "recurrent neural networks an embedded computing perspective"):
  with members:
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "single stream parallelization of recurrent neural networks for low power and fast inference"
    - "effective quantization approaches for recurrent neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "tensor decomposition for compressing recurrent neural network"

Connected through hyperedge HE-2 (induced by source paper "towards compact and robust deep neural networks"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "scaling provable adversarial defenses"
    - "formal security analysis of neural networks using symbolic intervals"
    - "sparse dnns with improved adversarial robustness"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "a dual approach to scalable verification of deep networks"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-3 (induced by source paper "machine learning at the network edge a survey"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "network offloading policies for cloud robotics a learning based approach"
    - "deep learning at the edge"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "edgelens deep learning based object detection in integrated iot fog and cloud computing environments"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"

Connected through hyperedge HE-4 (induced by source paper "sparsity meets robustness channel pruning for the feynman kac formalism principled robust deep neural nets"):
  with members:
    - "adversarial robustness vs model compression or both"
    - "robust sparse regularization simultaneously optimizing neural network robustness and compactness"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarially trained model compression when robustness meets efficiency"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "sparse dnns with improved adversarial robustness"
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "model compression via distillation and quantization"

Connected through hyperedge HE-5 (induced by source paper "model pruning enables efficient federated learning on edge devices"):
  with members:
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "advances and open problems in federated learning"
    - "performance analysis and characterization of training deep learning models on nvidia tx2"
    - "prunetrain gradual structured pruning from scratch for faster neural network training"
    - "distilling on device intelligence at the network edge"
    - "federated learning challenges methods and future directions"
    - "prunetrain fast neural network training by dynamic sparse model reconfiguration"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-6 (induced by source paper "zygarde time sensitive on device deep intelligence on intermittently powered systems"):
  with members:
    - "alpaca intermittent execution without checkpoints"
    - "rethinking floating point for deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "adaptive neural networks for efficient inference"
    - "rdeepsense reliable deep mobile computing models with uncertainty estimations"
    - "projectionnet learning efficient on device deep networks using neural projections"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-7 (induced by source paper "distilling knowledge from a deep pose regressor network"):
  with members:
    - "learning monocular visual odometry through geometry aware curriculum learning"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "model compression via distillation and quantization"
    - "self supervised knowledge distillation using singular value decomposition"
    - "ganvo unsupervised deep monocular visual odometry and depth estimation with generative adversarial networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "prior activation distribution pad a versatile representation to utilize dnn hidden units"):
  with members:
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "representer point selection for explaining deep neural networks"
    - "evidential deep learning to quantify classification uncertainty"
    - "network dissection quantifying interpretability of deep visual representations"
    - "a survey on deep learning in medical image analysis"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "pruning convolutional neural networks for resource efficient inference"
    - "benchmarking deep reinforcement learning for continuous control"

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)