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: "efficient deep learning on multi source private data"
  Abstract: Machine learning models benefit from large and diverse datasets. Using such datasets, however, often requires trusting a centralized data aggregator. For sensitive applications like healthcare and finance this is undesirable as it could compromise patient privacy or divulge trade secrets. Recent advances in secure and privacy-preserving computation, including trusted hardware enclaves and differential privacy, offer a way for mutually distrusting parties to efficiently train a machine learning model without revealing the training data. In this work, we introduce Myelin, a deep learning framework which combines these privacy-preservation primitives, and use it to establish a baseline level of performance for fully private machine learning.

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 "evaluating differentially private machine learning in practice"):
  with members:
    - "differentially private model publishing for deep learning"
    - "privacy preserving collaborative deep learning with irregular participants"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "dp admm admm based distributed learning with differential privacy"
    - "how to backdoor federated learning"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "detecting violations of differential privacy"

Connected through hyperedge HE-3 (induced by source paper "key protected classification for collaborative learning"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "exploiting unintended feature leakage in collaborative learning"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "adversarial attacks and defences a survey"
    - "privacy preserving machine learning through data obfuscation"
    - "differentially private generative adversarial network"
    - "personalized and private peer to peer machine learning"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

Connected through hyperedge HE-4 (induced by source paper "a roadmap toward the resilient internet of things for cyber physical systems"):
  with members:
    - "model based verification and validation of an autonomous vehicle system"
    - "deepmarks a digital fingerprinting framework for deep neural networks"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "reachability analysis and safety verification for neural network control systems"
    - "introduction of a hybrid monitor to cyber physical systems"
    - "verification for machine learning autonomy and neural networks survey"
    - "re purposing compact neuronal circuit policies to govern reinforcement learning tasks"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"

Connected through hyperedge HE-5 (induced by source paper "privacy preserving multi party contextual bandits"):
  with members:
    - "towards federated learning at scale system design"
    - "privacy preserving classification of personal text messages with secure multi party computation an application to hate speech detection"
    - "differentially private contextual linear bandits"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "gazelle a low latency framework for secure neural network inference"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "differentially private online learning"

Connected through hyperedge HE-6 (induced by source paper "privacy preserving contextual bandits"):
  with members:
    - "towards federated learning at scale system design"
    - "gazelle a low latency framework for secure neural network inference"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "differentially private online learning"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-7 (induced by source paper "securing input data of deep learning inference systems via partitioned enclave execution"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "chiron privacy preserving machine learning as a service"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "the circle game scalable private membership test using trusted hardware"
    - "opening the black box of deep neural networks via information"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "software grand exposure sgx cache attacks are practical"

Connected through hyperedge HE-8 (induced by source paper "reaching data confidentiality and model accountability on the caltrain"):
  with members:
    - "contamination attacks and mitigation in multi party machine learning"
    - "exploiting unintended feature leakage in collaborative learning"
    - "chiron privacy preserving machine learning as a service"
    - "how to backdoor federated learning"
    - "mitigating sybils in federated learning poisoning"
    - "securing input data of deep learning inference systems via partitioned enclave execution"
    - "the circle game scalable private membership test using trusted hardware"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"

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)