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: "the role of interactivity in local differential privacy"
  Abstract: We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols. Sequentially interactive protocols may query users adaptively in sequence, but they cannot return to previously queried users. The vast majority of existing lower bounds for local differential privacy apply only to sequentially interactive protocols, and before this paper it was not known whether fully interactive protocols were more powerful. We resolve this question. First, we classify locally private protocols by their compositionality, the multiplicative factor $k \geq 1$ by which the sum of a protocol's single-round privacy parameters exceeds its overall privacy guarantee. We then show how to efficiently transform any fully interactive $k$-compositional protocol into an equivalent sequentially interactive protocol with an $O(k)$ blowup in sample complexity. Next, we show that our reduction is tight by exhibiting a family of problems such that for any $k$, there is a fully interactive $k$-compositional protocol which solves the problem, while no sequentially interactive protocol can solve the problem without at least an $\tilde \Omega(k)$ factor more examples. We then turn our attention to hypothesis testing problems. We show that for a large class of compound hypothesis testing problems --- which include all simple hypothesis testing problems as a special case --- a simple noninteractive test is optimal among the class of all (possibly fully interactive) tests.

Connected through hyperedge HE-1 (induced by source paper "locally private hypothesis selection"):
  with members:
    - "private hypothesis selection"
    - "pan private uniformity testing"
    - "sorted top k in rounds"
    - "improved differentially private analysis of variance"
    - "locally private learning without interaction requires separation"
    - "exponential separations in local differential privacy through communication complexity"
    - "private identity testing for high dimensional distributions"
    - "the optimal approximation factor in density estimation"

Connected through hyperedge HE-2 (induced by source paper "an equivalence between private classification and online prediction"):
  with members:
    - "efficient noise tolerant and private learning via boosting"
    - "a necessary and sufficient stability notion for adaptive generalization"
    - "passing tests without memorizing two models for fooling discriminators"
    - "introduction to online convex optimization"
    - "privately learning thresholds closing the exponential gap"
    - "privately answering classification queries in the agnostic pac model"
    - "private learning implies online learning an efficient reduction"
    - "private pac learning implies finite littlestone dimension"

Connected through hyperedge HE-3 (induced by source paper "online agnostic boosting via regret minimization"):
  with members:
    - "boosting for dynamical systems"
    - "passing tests without memorizing two models for fooling discriminators"
    - "introduction to online convex optimization"
    - "online control with adversarial disturbances"
    - "private learning implies online learning an efficient reduction"
    - "logarithmic regret for online control"
    - "the nonstochastic control problem"
    - "private pac learning implies finite littlestone dimension"

Connected through hyperedge HE-4 (induced by source paper "closure properties for private classification and online prediction"):
  with members:
    - "a new analysis of differential privacy s generalization guarantees"
    - "privately learning thresholds closing the exponential gap"
    - "private learning implies online learning an efficient reduction"
    - "private pac learning implies finite littlestone dimension"
    - "generalization for adaptively chosen estimators via stable median"
    - "online learning via differential privacy"
    - "differentially private release and learning of threshold functions"
    - "algorithmic stability for adaptive data analysis"

Connected through hyperedge HE-5 (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"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "tight lower bounds for locally differentially private selection"

Connected through hyperedge HE-6 (induced by source paper "interaction is necessary for distributed learning with privacy or communication constraints"):
  with members:
    - "inference under information constraints ii communication constraints and shared randomness"
    - "distributed learning with sublinear communication"
    - "locally private learning without interaction requires separation"
    - "estimating smooth glm in non interactive local differential privacy model with public unlabeled data"
    - "exponential separations in local differential privacy through communication complexity"
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "learning without interaction requires separation"
    - "hadamard response estimating distributions privately efficiently and with little communication"

Connected through hyperedge HE-7 (induced by source paper "pan private uniformity testing"):
  with members:
    - "exponential separations in local differential privacy"
    - "communication and memory efficient testing of discrete distributions"
    - "test without trust optimal locally private distribution testing"
    - "usable differential privacy a case study with psi"
    - "distributed differential privacy via shuffling"
    - "differentially private testing of identity and closeness of discrete distributions"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"

Connected through hyperedge HE-8 (induced by source paper "locally private learning without interaction requires separation"):
  with members:
    - "exponential separations in local differential privacy"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "collecting telemetry data privately"
    - "distributed mean estimation with limited communication"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "the limitations of optimization from samples"
    - "algorithmic stability for adaptive data analysis"

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)