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: "ektelo a framework for defining differentially private computations"
  Abstract: The adoption of differential privacy is growing but the complexity of designing private, efficient and accurate algorithms is still high. We propose a novel programming framework and system, Ektelo, for implementing both existing and new privacy algorithms. For the task of answering linear counting queries, we show that nearly all existing algorithms can be composed from operators, each conforming to one of a small number of operator classes. While past programming frameworks have helped to ensure the privacy of programs, the novelty of our framework is its significant support for authoring accurate and efficient (as well as private) programs. After describing the design and architecture of the Ektelo system, we show that Ektelo is expressive, that it allows for safer implementations through code reuse, and that it allows both privacy novices and experts to easily design algorithms. We demonstrate the use of Ektelo by designing several new state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "guidelines for implementing and auditing differentially private systems"):
  with members:
    - "differentially private objective perturbation beyond smoothness and convexity"
    - "identifying cache based side channels through secret augmented abstract interpretation"
    - "fuzzi a three level logic for differential privacy"
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "private selection from private candidates"
    - "detecting violations of differential privacy"
    - "how to use heuristics for differential privacy"
    - "synthesizing coupling proofs of differential privacy"

Connected through hyperedge HE-2 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy"):
  with members:
    - "graphical model based estimation and inference for differential privacy"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "the power of factorization mechanisms in local and central differential privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "optimizing error of high dimensional statistical queries under differential privacy"
    - "towards instance optimal private query release"
    - "linear queries estimation with local differential privacy"
    - "practical locally private heavy hitters"

Connected through hyperedge HE-3 (induced by source paper "crypt epsilon crypto assisted differential privacy on untrusted servers"):
  with members:
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "mvg mechanism differential privacy under matrix valued query"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "shrinkwrap differentially private query processing in private data federations"
    - "locally differentially private heavy hitter identification"
    - "renyi differential privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "understanding the sparse vector technique for differential privacy"

Connected through hyperedge HE-4 (induced by source paper "outis crypto assisted differential privacy on untrusted servers"):
  with members:
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "mvg mechanism differential privacy under matrix valued query"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "shrinkwrap differentially private query processing in private data federations"
    - "locally differentially private heavy hitter identification"
    - "marginal release under local differential privacy"
    - "cryptodl deep neural networks over encrypted data"
    - "prochlo strong privacy for analytics in the crowd"

Connected through hyperedge HE-5 (induced by source paper "free gap information from the differentially private sparse vector and noisy max mechanisms"):
  with members:
    - "proving differential privacy with shadow execution"
    - "private selection from private candidates"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "synthesizing coupling proofs of differential privacy"
    - "renyi differential privacy"
    - "towards practical differential privacy for sql queries"
    - "prochlo strong privacy for analytics in the crowd"

Connected through hyperedge HE-6 (induced by source paper "a programming framework for differential privacy with accuracy concentration bounds"):
  with members:
    - "fuzzi a three level logic for differential privacy"
    - "proving differential privacy with shadow execution"
    - "synthesizing coupling proofs of differential privacy"
    - "apex accuracy aware differentially private data exploration"
    - "renyi differential privacy"
    - "lightdp towards automating differential privacy proofs"
    - "advanced probabilistic couplings for differential privacy"
    - "concentrated differential privacy simplifications extensions and lower bounds"

Connected through hyperedge HE-7 (induced by source paper "proving differential privacy with shadow execution"):
  with members:
    - "property testing for differential privacy"
    - "detecting violations of differential privacy"
    - "synthesizing coupling proofs of differential privacy"
    - "relational symbolic execution"
    - "renyi differential privacy"
    - "towards practical differential privacy for sql queries"
    - "lightdp towards automating differential privacy proofs"
    - "advanced probabilistic couplings for differential privacy"

Connected through hyperedge HE-8 (induced by source paper "fuzzi a three level logic for differential privacy"):
  with members:
    - "approximate span liftings compositional semantics for relaxations of differential privacy"
    - "detecting violations of differential privacy"
    - "synthesizing coupling proofs of differential privacy"
    - "learning differentially private recurrent language models"
    - "towards practical differential privacy for sql queries"
    - "lightdp towards automating differential privacy proofs"
    - "probabilistic couplings for probabilistic reasoning"
    - "psi ψ a private data sharing interface"

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.DB(Databases)