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: "chiron privacy preserving machine learning as a service"
  Abstract: Major cloud operators offer machine learning (ML) as a service, enabling customers who have the data but not ML expertise or infrastructure to train predictive models on this data. Existing ML-as-a-service platforms require users to reveal all training data to the service operator. We design, implement, and evaluate Chiron, a system for privacy-preserving machine learning as a service. First, Chiron conceals the training data from the service operator. Second, in keeping with how many existing ML-as-a-service platforms work, Chiron reveals neither the training algorithm nor the model structure to the user, providing only black-box access to the trained model. Chiron is implemented using SGX enclaves, but SGX alone does not achieve the dual goals of data privacy and model confidentiality. Chiron runs the standard ML training toolchain (including the popular Theano framework and C compiler) in an enclave, but the untrusted model-creation code from the service operator is further confined in a Ryoan sandbox to prevent it from leaking the training data outside the enclave. To support distributed training, Chiron executes multiple concurrent enclaves that exchange model parameters via a parameter server. We evaluate Chiron on popular deep learning models, focusing on benchmark image classification tasks such as CIFAR and ImageNet, and show that its training performance and accuracy of the resulting models are practical for common uses of ML-as-a-service.

Connected through hyperedge HE-1 (induced by source paper "optimizing privacy preserving outsourced convolutional neural network predictions"):
  with members:
    - "xonn xnor based oblivious deep neural network inference"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "gazelle a low latency framework for secure neural network inference"
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"
    - "logan membership inference attacks against generative models"
    - "deepsecure scalable provably secure deep learning"

Connected through hyperedge HE-2 (induced by source paper "cryptflow secure tensorflow inference"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "ngraph he2 a high throughput framework for neural network inference on encrypted data"
    - "a hybrid approach to secure function evaluation using sgx"
    - "helen maliciously secure coopetitive learning for linear models"
    - "trident efficient 4pc framework for privacy preserving machine learning"
    - "astra high throughput 3pc over rings with application to secure prediction"
    - "xonn xnor based oblivious deep neural network inference"

Connected through hyperedge HE-3 (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"
    - "spectre attacks exploiting speculative execution"
    - "gazelle a low latency framework for secure neural network inference"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "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-4 (induced by source paper "privacy preserving blockchain based federated learning with differential data sharing"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "a tutorial on computing t closeness"
    - "privacy preserving deep learning via weight transmission"
    - "a secure and robust scheme for sharing confidential information in iot systems"
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "building scalable decentralized payment systems"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"

Connected through hyperedge HE-5 (induced by source paper "towards characterizing and limiting information exposure in dnn layers"):
  with members:
    - "exploiting unintended feature leakage in collaborative learning"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "securing input data of deep learning inference systems via partitioned enclave execution"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-6 (induced by source paper "enforcing private data usage control with blockchain and attested off chain contract execution"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "private data objects an overview"
    - "sok sanitizing for security"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "use privacy in data driven systems theory and experiments with machine learnt programs"
    - "enhancing bitcoin security and performance with strong consistency via collective signing"
    - "enigma decentralized computation platform with guaranteed privacy"

Connected through hyperedge HE-7 (induced by source paper "the value of collaboration in convex machine learning with differential privacy"):
  with members:
    - "dp admm admm based distributed learning with differential privacy"
    - "privacy preserving machine learning through data obfuscation"
    - "learning differentially private recurrent language models"
    - "on the differential privacy of bayesian inference"
    - "differentially private distributed optimization"
    - "functional mechanism regression analysis under differential privacy"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

Connected through hyperedge HE-8 (induced by source paper "shredder learning noise distributions to protect inference privacy"):
  with members:
    - "using self supervised learning can improve model robustness and uncertainty"
    - "gazelle a low latency framework for secure neural network inference"
    - "security risks in deep learning implementations"
    - "self supervised learning for spinal mris"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "how transferable are features in deep neural networks"

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)