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: "security and privacy issues in deep learning"
  Abstract: With the development of machine learning, expectations for artificial intelligence (AI) technology are increasing day by day. In particular, deep learning has shown enriched performance results in a variety of fields. There are many applications that are closely related to our daily life, such as making significant decisions in application area based on predictions or classifications, in which a deep learning (DL) model could be relevant. Hence, if a DL model causes mispredictions or misclassifications due to malicious external influences, it can cause very large difficulties in real life. Moreover, training deep learning models involves relying on an enormous amount of data and the training data often includes sensitive information. Therefore, deep learning models should not expose the privacy of such data. In this paper, we reviewed the threats and developed defense methods on the security of the models and the data privacy under the notion of SPAI: Secure and Private AI. We also discuss current challenges and open issues.

Connected through hyperedge HE-1 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "energy recycling blockchain with proof of deep learning"
    - "decentralized and collaborative ai on blockchain"
    - "sok transparent dishonesty front running attacks on blockchain"
    - "reliable federated learning for mobile networks"
    - "keystone an open framework for architecting tees"
    - "tensorscone a secure tensorflow framework using intel sgx"
    - "coin ai a proof of useful work scheme for blockchain based distributed deep learning"

Connected through hyperedge HE-2 (induced by source paper "adversarial security attacks and perturbations on machine learning and deep learning methods"):
  with members:
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "designing adversarially resilient classifiers using resilient feature engineering"
    - "security for machine learning based systems attacks and challenges during training and inference"
    - "security matters a survey on adversarial machine learning"
    - "deep text classification can be fooled"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "adversarial malware binaries evading deep learning for malware detection in executables"

Connected through hyperedge HE-3 (induced by source paper "puvae a variational autoencoder to purify adversarial examples"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "structured adversarial attack towards general implementation and better interpretability"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "domain adaptation using adversarial learning for autonomous navigation"
    - "magnet a two pronged defense against adversarial examples"
    - "autonomous uav navigation with domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarial training for disease prediction from electronic health records with missing data"

Connected through hyperedge HE-4 (induced by source paper "crypto oriented neural architecture design"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "xonn xnor based oblivious deep neural network inference"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "gazelle a low latency framework for secure neural network inference"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"

Connected through hyperedge HE-5 (induced by source paper "slsgd secure and efficient distributed on device machine learning"):
  with members:
    - "analyzing federated learning through an adversarial lens"
    - "federated learning strategies for improving communication efficiency"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "how to backdoor federated learning"
    - "mitigating sybils in federated learning poisoning"
    - "phocas dimensional byzantine resilient stochastic gradient descent"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"

Connected through hyperedge HE-6 (induced by source paper "free riders in federated learning attacks and defenses"):
  with members:
    - "anomaly detection using autoencoders in high performance computing systems"
    - "mobile edge computing blockchain and reputation based crowdsourcing iot federated learning a secure decentralized and privacy preserving system"
    - "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation"
    - "analyzing federated learning through an adversarial lens"
    - "dancing in the dark private multi party machine learning in an untrusted setting"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "federated learning for mobile keyboard prediction"
    - "how to backdoor federated learning"

Connected through hyperedge HE-7 (induced by source paper "anomigan generative adversarial networks for anonymizing private medical data"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "cryptodl deep neural networks over encrypted data"
    - "deep learning in bioinformatics"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "the random oracle methodology revisited"

Connected through hyperedge HE-8 (induced by source paper "morphed learning towards privacy preserving for deep learning based applications"):
  with members:
    - "deep learning a critical appraisal"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "mask r cnn"
    - "channel pruning for accelerating very deep neural networks"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "deep residual learning for 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.CR(Cryptography and Security)