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: "scalable and differentially private distributed aggregation in the shuffled model"
  Abstract: Federated learning promises to make machine learning feasible on distributed, private datasets by implementing gradient descent using secure aggregation methods. The idea is to compute a global weight update without revealing the contributions of individual users. #R##N#Current practical protocols for secure aggregation work in an "honest but curious" setting where a curious adversary observing all communication to and from the server cannot learn any private information assuming the server is honest and follows the protocol. #R##N#A more scalable and robust primitive for privacy-preserving protocols is shuffling of user data, so as to hide the origin of each data item. Highly scalable and secure protocols for shuffling, so-called mixnets, have been proposed as a primitive for privacy-preserving analytics in the Encode-Shuffle-Analyze framework by Bittau et al. Recent papers by Cheu et al. and Balle et al. have formalized the "shuffled model" and suggested protocols for secure aggregation that achieve differential privacy guarantees. Their protocols come at a cost, though: Either the expected aggregation error or the amount of communication per user scales as a polynomial $n^{\Omega(1)}$ in the number of users $n$. #R##N#In this paper we propose simple and more efficient protocol for aggregation in the shuffled model, where communication as well as error increases only polylogarithmically in $n$. Our new technique is a conceptual "invisibility cloak" that makes users' data almost indistinguishable from random noise while introducing zero distortion on the sum.

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"
    - "federated learning strategies for improving communication efficiency"
    - "amplification by shuffling from local to central differential privacy via anonymity"

Connected through hyperedge HE-2 (induced by source paper "private summation in the multi message shuffle model"):
  with members:
    - "on the power of multiple anonymous messages"
    - "the privacy blanket of the shuffle model"
    - "private aggregation from fewer anonymous messages"
    - "manipulation attacks in local differential privacy"
    - "distributed differential privacy via shuffling"
    - "prochlo strong privacy for analytics in the crowd"
    - "calibrating data to sensitivity in private data analysis"
    - "what can we learn privately"

Connected through hyperedge HE-3 (induced by source paper "on the power of multiple anonymous messages"):
  with members:
    - "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"
    - "practical and robust privacy amplification with multi party differential privacy"
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "private aggregation from fewer anonymous messages"
    - "hadamard response estimating distributions privately efficiently and with little communication"

Connected through hyperedge HE-4 (induced by source paper "the power of synergy in differential privacy combining a small curator with local randomizers"):
  with members:
    - "locally private k means clustering"
    - "blender enabling local search with a hybrid differential privacy model"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "exponential separations in local differential privacy through communication complexity"
    - "the role of interactivity in local differential privacy"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "tight lower bounds for locally differentially private selection"

Connected through hyperedge HE-5 (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"
    - "federated heavy hitters discovery with differential privacy"
    - "enhancing the privacy of federated learning with sketching"
    - "differentially private meta learning"

Connected through hyperedge HE-6 (induced by source paper "private heavy hitters and range queries in the shuffled model"):
  with members:
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "tight lower bounds for locally differentially private selection"
    - "answering range queries under local differential privacy"
    - "practical locally private heavy hitters"
    - "marginal release under local differential privacy"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "collecting telemetry data privately"

Connected through hyperedge HE-7 (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"
    - "federated learning strategies for improving communication efficiency"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "distributed differential privacy via shuffling"
    - "prio private robust and scalable computation of aggregate statistics"

Connected through hyperedge HE-8 (induced by source paper "separating local shuffled differential privacy via histograms"):
  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"
    - "the role of interactivity in local differential privacy"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "distributed differential privacy via shuffling"
    - "prochlo strong privacy for analytics in the crowd"
    - "simultaneous private learning of multiple concepts"

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)