human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "nearly optimal private convolution"):
  Abstract: We study computing the convolution of a private input $x$ with a public input $h$, while satisfying the guarantees of $(\epsilon, \delta)$-differential privacy. Convolution is a fundamental operation, intimately related to Fourier Transforms. In our setting, the private input may represent a time series of sensitive events or a histogram of a database of confidential personal information. Convolution then captures important primitives including linear filtering, which is an essential tool in time series analysis, and aggregation queries on projections of the data. #R##N#We give a nearly optimal algorithm for computing convolutions while satisfying $(\epsilon, \delta)$-differential privacy. Surprisingly, we follow the simple strategy of adding independent Laplacian noise to each Fourier coefficient and bounding the privacy loss using the composition theorem of Dwork, Rothblum, and Vadhan. We derive a closed form expression for the optimal noise to add to each Fourier coefficient using convex programming duality. Our algorithm is very efficient -- it is essentially no more computationally expensive than a Fast Fourier Transform. #R##N#To prove near optimality, we use the recent discrepancy lowerbounds of Muthukrishnan and Nikolov and derive a spectral lower bound using a characterization of discrepancy in terms of determinants.

Includes members:
  Member "faster private release of marginals on small databases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the power of factorization mechanisms in local and central differential privacy")
  Member "private decayed predicate sums on streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "money cannot buy everything trading infinite location data streams with bounded individual privacy loss")
  Member "the geometry of differential privacy the sparse and approximate cases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "optimal private halfspace counting via discrepancy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the power of multiple anonymous messages")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locally private hypothesis selection")
  Member "an adaptive mechanism for accurate query answering under differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "faster algorithms for privately releasing marginals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "data dependent differentially private parameter learning for directed graphical models")
  Member "link prediction by de anonymization how we won the kaggle social network challenge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks")

Question: Please tell me which class the source paper 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)