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: "practical differentially private top k selection with pay what you get composition"
  Abstract: We study the problem of top-$k$ selection over a large domain universe subject to user-level differential privacy. Typically, the exponential mechanism or report noisy max are the algorithms used to solve this problem. However, these algorithms require querying the database for the count of each domain element. We focus on the setting where the data domain is unknown, which is different than the setting of frequent itemsets where an apriori type algorithm can help prune the space of domain elements to query. We design algorithms that ensures (approximate) $(\epsilon,\delta>0)$-differential privacy and only needs access to the true top-$\bar{k}$ elements from the data for any chosen $\bar{k} \geq k$. This is a highly desirable feature for making differential privacy practical, since the algorithms require no knowledge of the domain. We consider both the setting where a user's data can modify an arbitrary number of counts by at most 1, i.e. unrestricted sensitivity, and the setting where a user's data can modify at most some small, fixed number of counts by at most 1, i.e. restricted sensitivity. Additionally, we provide a pay-what-you-get privacy composition bound for our algorithms. That is, our algorithms might return fewer than $k$ elements when the top-$k$ elements are queried, but the overall privacy budget only decreases by the size of the outcome set.

Connected through hyperedge HE-1 (induced by source paper "federating recommendations using differentially private prototypes"):
  with members:
    - "advances and open problems in federated learning"
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "applied federated learning improving google keyboard query suggestions"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "federated learning for mobile keyboard prediction"
    - "federated tensor factorization for computational phenotyping"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"

Connected through hyperedge HE-2 (induced by source paper "linkedin s audience engagements api a privacy preserving data analytics system at scale"):
  with members:
    - "optimal differential privacy composition for exponential mechanisms and the cost of adaptivity"
    - "pripearl a framework for privacy preserving analytics and reporting at linkedin"
    - "towards practical differential privacy for sql queries"
    - "collecting telemetry data privately"
    - "psi ψ a private data sharing interface"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "the composition theorem for differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-3 (induced by source paper "optimal differential privacy composition for exponential mechanisms and the cost of adaptivity"):
  with members:
    - "gaussian differential privacy"
    - "the role of interactivity in local differential privacy"
    - "privacy odometers and filters pay as you go composition"
    - "the complexity of computing the optimal composition of differential privacy"
    - "the composition theorem for differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "what can we learn privately"

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)