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: "private decayed predicate sums on streams"
  Abstract: In many monitoring applications, recent data is more important than distant data. How does this affect privacy of data analysis? We study a general class of data analyses --- predicate sums --- in this context.   Formally, we study the problem of estimating predicate sums privately, for sliding windows and other decay models. While we require accuracy in analysis with respect to the decayed sums, we still want differential privacy for the entire past. This is challenging because window sums are not monotonic or even near-monotonic as the problems studied previously [DPNR10].   We present accurate e-differentially private algorithms for decayed sums. For window and exponential decay sums, our algorithms are accurate up to additive 1/e and polylog terms in the range of the computed function; for polynomial decay sums which are technically more challenging because partial solutions do not compose easily, our algorithms incur additional relative error. Our algorithm for polynomial decay sums generalizes to arbitrary decay sum functions. The algorithm crucially relies on our solution for the window sum problem as a subroutine. Further, we show lower bounds, tight within polylog factors and tight with respect to the dependence on the probability of error. Our results are obtained via a natural dyadic tree we maintain, but the crux is we treat the tree data structure in non-uniform manner.   We also extend our study and consider the "dual" question of maintaining conventional running sums on the entire data thus far, but when privacy constraints expire with time. We define a new model of privacy with expiration and consider the problems of designing accurate running sum and linear map algorithms in this model. Now the goal is to design algorithms whose accuracy guarantees scale with the size of the privacy window. We reduce running sum with a privacy window W to window sum without privacy expiration, and characterize the accuracy of output perturbation for general linear maps with privacy window W.

Connected through hyperedge HE-1 (induced by source paper "money cannot buy everything trading infinite location data streams with bounded individual privacy loss"):
  with members:
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "quantifying differential privacy under temporal correlations"
    - "how to balance privacy and money through pricing mechanism in personal data market"
    - "a theory of pricing private data"
    - "selling privacy at auction"

Connected through hyperedge HE-2 (induced by source paper "temporally discounted differential privacy for evolving datasets on an infinite horizon"):
  with members:
    - "private continual release of real valued data streams"
    - "differential privacy for growing databases"
    - "local differential privacy for evolving data"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-3 (induced by source paper "quantifying differential privacy in continuous data release under temporal correlations"):
  with members:
    - "quantifying differential privacy under temporal correlations"
    - "pufferfish privacy mechanisms for correlated data"
    - "protecting locations with differential privacy under temporal correlations"

Connected through hyperedge HE-4 (induced by source paper "differential private stream processing of energy consumption"):
  with members:
    - "collecting telemetry data privately"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "optimality of the laplace mechanism in differential privacy"
    - "geo indistinguishability differential privacy for location based systems"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-5 (induced by source paper "optstream releasing time series privately"):
  with members:
    - "collecting telemetry data privately"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "optimality of the laplace mechanism in differential privacy"
    - "geo indistinguishability differential privacy for location based systems"

Connected through hyperedge HE-6 (induced by source paper "private continual release of real valued data streams"):
  with members:
    - "privacy in information rich intelligent infrastructure"
    - "psi ψ a private data sharing interface"

Connected through hyperedge HE-7 (induced by source paper "quantifying differential privacy under temporal correlations"):
  with members:
    - "protecting locations with differential privacy under temporal correlations"
    - "prolonging the hide and seek game optimal trajectory privacy for location based services"
    - "calibrating data to sensitivity in private data analysis"
    - "web mining research a survey"

Connected through hyperedge HE-8 (induced by source paper "on differentially private online collaborative recommendation systems"):
  with members:
    - "beating randomized response on incoherent matrices"
    - "beyond worst case analysis in private singular vector computation"
    - "differentially private online learning"
    - "a learning theory approach to non interactive database privacy"
    - "interactive privacy via the median mechanism"

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)