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: "differentially private draw and discard machine learning"
  Abstract: In this work, we propose a novel framework for privacy-preserving client-distributed machine learning. It is motivated by the desire to achieve differential privacy guarantees in the local model of privacy in a way that satisfies all systems constraints using asynchronous client-server communication and provides attractive model learning properties. We call it "Draw and Discard" because it relies on random sampling of models for load distribution (scalability), which also provides additional server-side privacy protections and improved model quality through averaging. We present the mechanics of client and server components of "Draw and Discard" and demonstrate how the framework can be applied to learning Generalized Linear models. We then analyze the privacy guarantees provided by our approach against several types of adversaries and showcase experimental results that provide evidence for the framework's viability in practical deployments.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving boosting in the local setting"):
  with members:
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "locally differentially private naive bayes classification"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "clustering algorithms for the centralized and local models"
    - "prochlo strong privacy for analytics in the crowd"
    - "collecting telemetry data privately"
    - "collecting and analyzing data from smart device users with local differential privacy"
    - "crowd ml a privacy preserving learning framework for a crowd of smart devices"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving image classification in the local setting"):
  with members:
    - "collecting telemetry data privately"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "collecting and analyzing data from smart device users with local differential privacy"
    - "pcanet a simple deep learning baseline for image classification"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "extremal mechanisms for local differential privacy"

Connected through hyperedge HE-3 (induced by source paper "orthogonal gradient descent for continual learning"):
  with members:
    - "prototype reminding for continual learning"
    - "uncertainty guided continual learning with bayesian neural networks"
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "towards federated learning at scale system design"
    - "variational prototype replays for continual learning"
    - "attention based structural plasticity"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"

Connected through hyperedge HE-4 (induced by source paper "towards federated learning at scale system design"):
  with members:
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "applied federated learning improving google keyboard query suggestions"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"
    - "federated learning for mobile keyboard prediction"
    - "how to backdoor federated learning"
    - "expanding the reach of federated learning by reducing client resource requirements"

Connected through hyperedge HE-5 (induced by source paper "privacy preserving bandits"):
  with members:
    - "encode shuffle analyze privacy revisited formalizations and empirical evaluation"
    - "guidelines for implementing and auditing differentially private systems"
    - "towards federated learning at scale system design"
    - "differential privacy for multi armed bandits what is it and what is its cost"
    - "federated learning strategies for improving communication efficiency"
    - "differentially private contextual linear bandits"
    - "achieving privacy in the adversarial multi armed bandit"
    - "corrupt bandits for preserving local privacy"

Connected through hyperedge HE-6 (induced by source paper "leaf a benchmark for federated settings"):
  with members:
    - "improving federated learning personalization via model agnostic meta learning"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "fair resource allocation in federated learning"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "on first order meta learning algorithms"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"

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)