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: "not just privacy improving performance of private deep learning in mobile cloud"
  Abstract: The increasing demand for on-device deep learning services calls for a highly efficient manner to deploy deep neural networks (DNNs) on mobile devices with limited capacity. The cloud-based solution is a promising approach to enabling deep learning applications on mobile devices where the large portions of a DNN are offloaded to the cloud. However, revealing data to the cloud leads to potential privacy risk. To benefit from the cloud data center without the privacy risk, we design, evaluate, and implement a cloud-based framework ARDEN which partitions the DNN across mobile devices and cloud data centers. A simple data transformation is performed on the mobile device, while the resource-hungry training and the complex inference rely on the cloud data center. To protect the sensitive information, a lightweight privacy-preserving mechanism consisting of arbitrary data nullification and random noise addition is introduced, which provides strong privacy guarantee. A rigorous privacy budget analysis is given. Nonetheless, the private perturbation to the original data inevitably has a negative impact on the performance of further inference on the cloud side. To mitigate this influence, we propose a noisy training method to enhance the cloud-side network robustness to perturbed data. Through the sophisticated design, ARDEN can not only preserve privacy but also improve the inference performance. To validate the proposed ARDEN, a series of experiments based on three image datasets and a real mobile application are conducted. The experimental results demonstrate the effectiveness of ARDEN. Finally, we implement ARDEN on a demo system to verify its practicality.

Connected through hyperedge HE-1 (induced by source paper "neural architecture search over decentralized data"):
  with members:
    - "towards federated learning at scale system design"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "deepcache principled cache for mobile deep vision"
    - "federated learning strategies for improving communication efficiency"
    - "leaf a benchmark for federated settings"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "path level network transformation for efficient architecture search"
    - "federated learning for mobile keyboard prediction"

Connected through hyperedge HE-2 (induced by source paper "deep quaternion features for privacy protection"):
  with members:
    - "adversarial neural network inversion via auxiliary knowledge alignment"
    - "complex valued neural networks for privacy protection"
    - "interpretable complex valued neural networks for privacy protection"
    - "quaternion convolutional neural networks"
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "exploiting unintended feature leakage in collaborative learning"
    - "gazelle a low latency framework for secure neural network inference"
    - "deep complex networks"

Connected through hyperedge HE-3 (induced by source paper "edge intelligence paving the last mile of artificial intelligence with edge computing"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "peer to peer federated learning on graphs"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "on device federated learning via blockchain and its latency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "communication compression for decentralized training"

Connected through hyperedge HE-4 (induced by source paper "edge intelligence the confluence of edge computing and artificial intelligence"):
  with members:
    - "convergence of edge computing and deep learning a comprehensive survey"
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "a graph neural network approach for scalable wireless power control"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "scan a scalable neural networks framework towards compact and efficient models"
    - "vehicular edge computing via deep reinforcement learning"
    - "openei an open framework for edge intelligence"
    - "uav communications for 5g and beyond recent advances and future trends"

Connected through hyperedge HE-5 (induced by source paper "transformative effects of iot blockchain and artificial intelligence on cloud computing evolution vision trends and open challenges"):
  with members:
    - "healthfog an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments"
    - "a deep learning based approach to vm behavior identification in cloud systems"
    - "edgelens deep learning based object detection in integrated iot fog and cloud computing environments"
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "cloud chaser real time deep learning computer vision on low computing power devices"
    - "a survey on the security of blockchain systems"
    - "container based cluster orchestration systems a taxonomy and future directions"
    - "sustainable cloud computing foundations and future directions"

Connected through hyperedge HE-6 (induced by source paper "overlearning reveals sensitive attributes"):
  with members:
    - "similarity of neural network representations revisited"
    - "privacy partitioning protecting user data during the deep learning inference phase"
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "exploiting unintended feature leakage in collaborative learning"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "isolating sources of disentanglement in variational autoencoders"
    - "privacy preserving neural representations of text"
    - "adversarial removal of demographic attributes from text data"

Connected through hyperedge HE-7 (induced by source paper "shredder learning noise to protect privacy with partial dnn inference on the edge"):
  with members:
    - "zombieload cross privilege boundary data sampling"
    - "chiron privacy preserving machine learning as a service"
    - "spectre attacks exploiting speculative execution"
    - "gazelle a low latency framework for secure neural network inference"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "opening the black box of deep neural networks via information"
    - "prochlo strong privacy for analytics in the crowd"

Connected through hyperedge HE-8 (induced by source paper "towards efficient and secure delivery of data for deep learning with privacy preserving"):
  with members:
    - "streaming end to end speech recognition for mobile devices"
    - "deep learning a critical appraisal"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "gazelle a low latency framework for secure neural network inference"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "deepsecure scalable provably secure deep learning"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "mask r cnn"

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)