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 "automated methods for checking differential privacy"):
  Abstract: Differential privacy is a de facto standard for statistical computations over databases that contain private data. The strength of differential privacy lies in a rigorous mathematical definition that guarantees individual privacy and yet allows for accurate statistical results. Thanks to its mathematical definition, differential privacy is also a natural target for formal analysis. A broad line of work uses logical methods for proving privacy. However, these methods are not complete, and only partially automated. A recent and complementary line of work uses statistical methods for finding privacy violations. However, the methods only provide statistical guarantees (but no proofs). #R##N#We propose the first decision procedure for checking the differential privacy of a non-trivial class of probabilistic computations. Our procedure takes as input a program P parametrized by a privacy budget $\epsilon$, and either proves differential privacy for all possible values of $\epsilon$ or generates a counterexample. In addition, our procedure applies both to $\epsilon$-differential privacy and $(\epsilon,\delta)$-differential privacy. Technically, the decision procedure is based on a novel and judicious encoding of the semantics of programs in our class into a decidable fragment of the first-order theory of the reals with exponentiation. We implement our procedure and use it for (dis)proving privacy bounds for many well-known examples, including randomized response, histogram, report noisy max and sparse vector.

Includes members:
  Member "bisimilarity distances for approximate differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the complexity of verifying loop free programs as differentially private")
  Member "detecting violations of differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "synthesizing coupling proofs of differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "advanced probabilistic couplings for differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "understanding the sparse vector technique for differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach")
  Member "a program logic for union bounds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unifying graded and parameterised monads")
  Member "proving differential privacy via probabilistic couplings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "locally private hypothesis selection")

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.CR(Cryptography and Security)