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 private statistics with succinct sketches"
  Abstract: Large-scale collection of contextual information is often essential in order to gather statistics, train machine learning models, and extract knowledge from data. The ability to do so in a {\em privacy-preserving} way -- i.e., without collecting fine-grained user data -- enables a number of additional computational scenarios that would be hard, or outright impossible, to realize without strong privacy guarantees. In this paper, we present the design and implementation of practical techniques for privately gathering statistics from large data streams. We build on efficient cryptographic protocols for private aggregation and on data structures for succinct data representation, namely, Count-Min Sketch and Count Sketch. These allow us to reduce the communication and computation complexity incurred by each data source (e.g., end-users) from linear to logarithmic in the size of their input, while introducing a parametrized upper-bounded error that does not compromise the quality of the statistics. We then show how to use our techniques, efficiently, to instantiate real-world privacy-friendly systems, supporting recommendations for media streaming services, prediction of user locations, and computation of median statistics for Tor hidden services.

Connected through hyperedge HE-1 (induced by source paper "pure differentially private summation from anonymous messages"):
  with members:
    - "on the power of multiple anonymous messages"
    - "improved summation from shuffling"
    - "separating local shuffled differential privacy via histograms"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "private aggregation from fewer anonymous messages"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-2 (induced by source paper "timecrypt encrypted data stream processing at scale with cryptographic access control"):
  with members:
    - "jedi many to many end to end encryption and key delegation for iot"
    - "prio private robust and scalable computation of aggregate statistics"
    - "streetx spatio temporal access control model for data"
    - "sok cryptographically protected database search"

Connected through hyperedge HE-3 (induced by source paper "efficient differentially private f_0 linear sketching"):
  with members:
    - "cardinality estimators do not preserve privacy"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-4 (induced by source paper "privacy for free communication efficient learning with differential privacy using sketches"):
  with members:
    - "towards federated learning at scale system design"
    - "total stochastic gradient algorithms and applications in reinforcement learning"
    - "communication efficient distributed sgd with sketching"
    - "secure computation for machine learning with spdz"
    - "federated learning challenges methods and future directions"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "federated heavy hitters discovery with differential privacy"
    - "enhancing the privacy of federated learning with sketching"

Connected through hyperedge HE-5 (induced by source paper "drynx decentralized secure verifiable system for statistical queries and machine learning on distributed datasets"):
  with members:
    - "helen maliciously secure coopetitive learning for linear models"
    - "evaluating differentially private machine learning in practice"
    - "hotstuff bft consensus in the lens of blockchain"
    - "dp admm admm based distributed learning with differential privacy"
    - "gazelle a low latency framework for secure neural network inference"
    - "plausible deniability for privacy preserving data synthesis"
    - "differentially private federated learning a client level perspective"
    - "towards practical differential privacy for sql queries"

Connected through hyperedge HE-6 (induced by source paper "private aggregation from fewer anonymous messages"):
  with members:
    - "private heavy hitters and range queries in the shuffled model"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "practical and robust privacy amplification with multi party differential privacy"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "federated learning strategies for improving communication efficiency"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "distributed differential privacy via shuffling"

Connected through hyperedge HE-7 (induced by source paper "enhancing the privacy of federated learning with sketching"):
  with members:
    - "towards federated learning at scale system design"
    - "federated learning challenges methods and future directions"
    - "privacy for free communication efficient learning with differential privacy using sketches"
    - "leaf a benchmark for federated settings"
    - "federated learning for mobile keyboard prediction"
    - "differentially private federated learning a client level perspective"
    - "learning differentially private recurrent language models"
    - "emnist an extension of mnist to handwritten letters"

Connected through hyperedge HE-8 (induced by source paper "beyond content analysis detecting targeted ads via distributed counting"):
  with members:
    - "on microtargeting socially divisive ads a case study of russia linked ad campaigns on facebook"
    - "tracing information flows between ad exchanges using retargeted ads"
    - "myadchoices bringing transparency and control to online advertising"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "i always feel like somebody s watching me measuring online behavioural advertising"
    - "adscape harvesting and analyzing online display ads"
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"
    - "xray enhancing the web s transparency with differential correlation"

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)