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: "test without trust optimal locally private distribution testing"
  Abstract: We study the problem of distribution testing when the samples can only be accessed using a locally differentially private mechanism and focus on two representative testing questions of identity (goodness-of-fit) and independence testing for discrete distributions. We are concerned with two settings: First, when we insist on using an already deployed, general-purpose locally differentially private mechanism such as the popular RAPPOR or the recently introduced Hadamard Response for collecting data, and must build our tests based on the data collected via this mechanism; and second, when no such restriction is imposed, and we can design a bespoke mechanism specifically for testing. For the latter purpose, we introduce the Randomized Aggregated Private Testing Optimal Response (RAPTOR) mechanism which is remarkably simple and requires only one bit of communication per sample. #R##N#We propose tests based on these mechanisms and analyze their sample complexities. Each proposed test can be implemented efficiently. In each case (barring one), we complement our performance bounds for algorithms with information-theoretic lower bounds and establish sample optimality of our proposed algorithm. A peculiar feature that emerges is that our sample-optimal algorithm based on RAPTOR uses public-coins, and any test based on RAPPOR or Hadamard Response, which are both private-coin mechanisms, requires significantly more samples.

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"
    - "the role of interactivity in local differential privacy"
    - "private identity testing for high dimensional distributions"

Connected through hyperedge HE-2 (induced by source paper "private identity testing for high dimensional distributions"):
  with members:
    - "lower bounds for testing graphical models colorings and antiferromagnetic ising models"
    - "efficiently estimating erdos renyi graphs with node differential privacy"
    - "improved differentially private analysis of variance"
    - "individual sensitivity preprocessing for data privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "learning and testing causal models with interventions"
    - "privately learning high dimensional distributions"
    - "locally private gaussian estimation"

Connected through hyperedge HE-3 (induced by source paper "the role of interactivity in local differential privacy"):
  with members:
    - "exponential separations in local differential privacy"
    - "the privacy blanket of the shuffle model"
    - "learning without interaction requires separation"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "towards instance optimal private query release"
    - "locally private gaussian estimation"
    - "tight lower bounds for locally differentially private selection"
    - "inference under information constraints i lower bounds from chi square contraction"

Connected through hyperedge HE-4 (induced by source paper "communication complexity in locally private distribution estimation and heavy hitters"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "detecting correlations with little memory and communication"
    - "distributed simulation and distributed inference"
    - "linear queries estimation with local differential privacy"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "practical locally private heavy hitters"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"

Connected through hyperedge HE-5 (induced by source paper "context aware local differential privacy"):
  with members:
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "federated heavy hitters discovery with differential privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "distributed differential privacy via shuffling"
    - "one sided differential privacy"
    - "practical locally private heavy hitters"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"

Connected through hyperedge HE-6 (induced by source paper "inference under information constraints ii communication constraints and shared randomness"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "sample optimal identity testing with high probability"
    - "a new approach for testing properties of discrete distributions"
    - "the optimality of correlated sampling"
    - "information theoretic lower bounds on bayes risk in decentralized estimation"
    - "decidability of non interactive simulation of joint distributions"

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"
    - "the role of interactivity in local differential privacy"
    - "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 "domain compression and its application to randomness optimal distributed goodness of fit"):
  with members:
    - "inference under information constraints ii communication constraints and shared randomness"
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "communication and memory efficient testing of discrete distributions"
    - "private identity testing for high dimensional distributions"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "locally private hypothesis testing"
    - "inference under information constraints i lower bounds from chi square contraction"

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.DS(Data Structures and Algorithms)