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: "a hybrid deep learning architecture for privacy preserving mobile analytics"
  Abstract: The increasing quality of smartphone cameras and variety of photo editing applications, in addition to the rise in popularity of image-centric social media, have all led to a phenomenal growth in mobile-based photography. Advances in computer vision and machine learning techniques provide a large number of cloud-based services with the ability to provide content analysis, face recognition, and object detection facilities to third parties. These inferences and analytics might come with undesired privacy risks to the individuals. #R##N#In this paper, we address a fundamental challenge: Can we utilize the local processing capabilities of modern smartphones efficiently to provide desired features to approved analytics services, while protecting against undesired inference attacks and preserving privacy on the cloud? We propose a hybrid architecture for a distributed deep learning model between the smartphone and the cloud. We rely on the Siamese network and machine learning approaches for providing privacy based on defined privacy constraints. We also use transfer learning techniques to evaluate the proposed method. Using the latest deep learning models for Face Recognition, Emotion Detection, and Gender Classification techniques, we demonstrate the effectiveness of our technique in providing highly accurate classification results for the desired analytics, while proving strong privacy guarantees.

Connected through hyperedge HE-1 (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"
    - "not just privacy improving performance of private deep learning in mobile cloud"

Connected through hyperedge HE-2 (induced by source paper "industrial scale privacy preserving deep neural network"):
  with members:
    - "privacy preserving pca for multiparty modeling"
    - "privacy preserving point of interest recommendation using decentralized matrix factorization"
    - "secure social recommendation based on secret sharing"
    - "practical privacy preserving poi recommendation"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "distributed learning of deep neural network over multiple agents"
    - "securing input data of deep learning inference systems via partitioned enclave execution"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "an adversarial learning framework for preserving users anonymity in face based emotion recognition"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "minimax filter learning to preserve privacy from inference attacks"

Connected through hyperedge HE-4 (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-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 "local differential privacy for deep learning"):
  with members:
    - "efficient privacy preservation of big data for accurate data mining"
    - "an energy driven network function virtualization for multi domain software defined networks"
    - "an efficient and scalable privacy preserving algorithm for big data and data streams"
    - "mvg mechanism differential privacy under matrix valued query"
    - "efficient data perturbation for privacy preserving and accurate data stream mining"
    - "towards practical differential privacy for sql queries"
    - "machine learning models that remember too much"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-7 (induced by source paper "deep learning on mobile devices a review"):
  with members:
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "the limits and potentials of deep learning for robotics"
    - "ai benchmark running deep neural networks on android smartphones"
    - "transfer learning for time series classification"
    - "an analysis of deep neural network models for practical applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "deep learning in robotics a review of recent research"

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"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "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)