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 approach to privacy preserving federated learning"
  Abstract: Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees. Rather, we need a federated learning system capable of preventing inference over both the messages exchanged during training and the final trained model while ensuring the resulting model also has acceptable predictive accuracy. Existing federated learning approaches either use secure multiparty computation (SMC) which is vulnerable to inference or differential privacy which can lead to low accuracy given a large number of parties with relatively small amounts of data each. In this paper, we present an alternative approach that utilizes both differential privacy and SMC to balance these trade-offs. Combining differential privacy with secure multiparty computation enables us to reduce the growth of noise injection as the number of parties increases without sacrificing privacy while maintaining a pre-defined rate of trust. Our system is therefore a scalable approach that protects against inference threats and produces models with high accuracy. Additionally, our system can be used to train a variety of machine learning models, which we validate with experimental results on 3 different machine learning algorithms. Our experiments demonstrate that our approach out-performs state of the art solutions.

Connected through hyperedge HE-1 (induced by source paper "think locally act globally federated learning with local and global representations"):
  with members:
    - "one shot federated learning"
    - "towards federated learning at scale system design"
    - "quantized epoch sgd for communication efficient distributed learning"
    - "improved adversarial learning for fair classification"
    - "agnostic federated learning"
    - "learning fair representations via an adversarial framework"
    - "gender bias in contextualized word embeddings"
    - "federated machine learning concept and applications"

Connected through hyperedge HE-2 (induced by source paper "performance analysis and optimization in privacy preserving federated learning"):
  with members:
    - "advances and open problems in federated learning"
    - "on safeguarding privacy and security in the framework of federated learning"
    - "federated reinforcement learning"
    - "deep leakage from gradients"
    - "federated learning with differential privacy algorithms and performance analysis"
    - "the value of collaboration in convex machine learning with differential privacy"
    - "federated learning challenges methods and future directions"
    - "age based scheduling policy for federated learning in mobile edge networks"

Connected through hyperedge HE-3 (induced by source paper "anonymizing data for privacy preserving federated learning"):
  with members:
    - "differentially private learning with adaptive clipping"
    - "differential privacy enabled federated learning for sensitive health data"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "how to backdoor federated learning"
    - "differentially private federated learning a client level perspective"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-4 (induced by source paper "a federated learning approach for mobile packet classification"):
  with members:
    - "one shot federated learning"
    - "towards federated learning at scale system design"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated heavy hitters discovery with differential privacy"
    - "analyzing federated learning through an adversarial lens"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "beyond inferring class representatives user level privacy leakage from federated learning"

Connected through hyperedge HE-5 (induced by source paper "effects of differential privacy and data skewness on membership inference vulnerability"):
  with members:
    - "demystifying learning rate polices for high accuracy training of deep neural networks"
    - "cross layer strategic ensemble defense against adversarial examples"
    - "differentially private model publishing for deep learning"
    - "evaluating differentially private machine learning in practice"
    - "robust or private adversarial training makes models more vulnerable to privacy attacks"
    - "deep neural network ensembles against deception ensemble diversity accuracy and robustness"
    - "exploiting unintended feature leakage in collaborative learning"
    - "beyond inferring class representatives user level privacy leakage from federated learning"

Connected through hyperedge HE-6 (induced by source paper "attack resistant federated learning with residual based reweighting"):
  with members:
    - "robust learning from untrusted sources"
    - "differentially private learning with adaptive clipping"
    - "byzantine stochastic gradient descent"
    - "exploiting unintended feature leakage in collaborative learning"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "how to backdoor federated learning"
    - "mitigating sybils in federated learning poisoning"

Connected through hyperedge HE-7 (induced by source paper "federated learning with differential privacy algorithms and performance analysis"):
  with members:
    - "on safeguarding privacy and security in the framework of federated learning"
    - "adaptive task allocation for asynchronous federated mobile edge learning"
    - "deep learning for distributed optimization applications to wireless resource management"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "the value of collaboration in convex machine learning with differential privacy"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"

Connected through hyperedge HE-8 (induced by source paper "high dimensional restrictive federated model selection with multi objective bayesian optimization over shifted distributions"):
  with members:
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "federated learning with non iid data"
    - "differentially private federated learning a client level perspective"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "communication efficient learning of deep networks from decentralized data"
    - "membership inference attacks against machine learning models"
    - "bayesian adaptive data analysis guarantees from subgaussianity"

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)