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: "optimizing error of high dimensional statistical queries under differential privacy"
  Abstract: Differentially private algorithms for answering sets of predicate counting queries on a sensitive database have many applications. Organizations that collect individual-level data, such as statistical agencies and medical institutions, use them to safely release summary tabulations. However, existing techniques are accurate only on a narrow class of query workloads, or are extremely slow, especially when analyzing more than one or two dimensions of the data. In this work we propose HDMM, a new differentially private algorithm for answering a workload of predicate counting queries, that is especially effective for higher-dimensional datasets. HDMM represents query workloads using an implicit matrix representation and exploits this compact representation to efficiently search (a subset of) the space of differentially private algorithms for one that answers the input query workload with high accuracy. We empirically show that HDMM can efficiently answer queries with lower error than state-of-the-art techniques on a variety of low and high dimensional datasets.

Connected through hyperedge HE-1 (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"
    - "towards instance optimal private query release"
    - "linear queries estimation with local differential privacy"
    - "ektelo a framework for defining differentially private computations"
    - "practical locally private heavy hitters"

Connected through hyperedge HE-2 (induced by source paper "the power of factorization mechanisms in local and central differential privacy"):
  with members:
    - "differentially private sql with bounded user contribution"
    - "towards instance optimal private query release"
    - "towards practical differential privacy for sql queries"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "an improved private mechanism for small databases"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "faster private release of marginals on small databases"

Connected through hyperedge HE-3 (induced by source paper "graphical model based estimation and inference for differential privacy"):
  with members:
    - "cipher construction of differentially private microdata from low dimensional histograms via solving linear equations with tikhonov regularization"
    - "differentially private learning of undirected graphical models using collective graphical models"
    - "a data and workload aware algorithm for range queries under differential privacy"
    - "dual query practical private query release for high dimensional data"
    - "calibrating data to sensitivity in private data analysis"
    - "faster algorithms for privately releasing marginals"
    - "differentially private grids for geospatial data"
    - "privately releasing conjunctions and the statistical query barrier"

Connected through hyperedge HE-4 (induced by source paper "capacity bounded differential privacy"):
  with members:
    - "a convex duality framework for gans"
    - "the inductive bias of restricted f gans"
    - "information privacy and stability in adaptive data analysis"
    - "generalization for adaptively chosen estimators via stable median"
    - "approximation and convergence properties of generative adversarial learning"
    - "renyi differential privacy"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-5 (induced by source paper "answering summation queries for numerical attributes under differential privacy"):
  with members:
    - "principled evaluation of differentially private algorithms using dpbench"
    - "optimal error of query sets under the differentially private matrix mechanism"
    - "boosting the accuracy of differentially private histograms through consistency"

Connected through hyperedge HE-6 (induced by source paper "ektelo a framework for defining differentially private computations"):
  with members:
    - "graphical model based estimation and inference for differential privacy"
    - "private selection from private candidates"
    - "synthesizing coupling proofs of differential privacy"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "lightdp towards automating differential privacy proofs"
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "differentially private bayesian programming"
    - "principled evaluation of differentially private algorithms using dpbench"

Connected through hyperedge HE-7 (induced by source paper "apex accuracy aware differentially private data exploration"):
  with members:
    - "ektelo a framework for defining differentially private computations"
    - "private exploration primitives for data cleaning"
    - "gradual release of sensitive data under differential privacy"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "a data and workload aware algorithm for range queries under differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "privbasis frequent itemset mining with differential privacy"

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)