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: "differential privacy for the analyst via private equilibrium computation"
  Abstract: We give new mechanisms for answering exponentially many queries from multiple analysts on a private database, while protecting dif- ferential privacy both for the individuals in the database and for the analysts. That is, our mechanism's answer to each query is nearly insensitive to changes in the queries asked by other analysts. Our mechanism is the first to offer differential privacy on the joint distribution over analysts' answers, providing privacy for data an- alysts even if the other data analysts collude or register multiple accounts. In some settings, we are able to achieve nearly optimal error rates (even compared to mechanisms which do not offer an- alyst privacy), and we are able to extend our techniques to handle non-linear queries. Our analysis is based on a novel view of the pri- vate query-release problem as a two-player zero-sum game, which may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "efficient noise tolerant and private learning via boosting"):
  with members:
    - "a new analysis of differential privacy s generalization guarantees"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "algorithmic stability for adaptive data analysis"
    - "privately solving linear programs"
    - "calibrating data to sensitivity in private data analysis"
    - "what can we learn privately"

Connected through hyperedge HE-2 (induced by source paper "how to use heuristics for differential privacy"):
  with members:
    - "unleashing linear optimizers for group fair learning and optimization"
    - "model agnostic private learning via stability"
    - "property testing for differential privacy"
    - "private pac learning implies finite littlestone dimension"
    - "privacy preserving prediction"
    - "online learning via differential privacy"
    - "adaptive learning with robust generalization guarantees"
    - "efficient algorithms for adversarial contextual learning"

Connected through hyperedge HE-3 (induced by source paper "jointly private convex programming"):
  with members:
    - "private matchings and allocations"
    - "privately solving linear programs"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "privacy and truthful equilibrium selection for aggregative games"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"

Connected through hyperedge HE-4 (induced by source paper "private matchings and allocations"):
  with members:
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "privacy and mechanism design"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"
    - "differentially private combinatorial optimization"

Connected through hyperedge HE-5 (induced by source paper "robust mediators in large games"):
  with members:
    - "finding any nontrivial coarse correlated equilibrium is hard"
    - "privacy and truthful equilibrium selection for aggregative games"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"
    - "a learning theory approach to non interactive database privacy"
    - "iterative constructions and private data release"
    - "privately releasing conjunctions and the statistical query barrier"
    - "interactive privacy via the median mechanism"

Connected through hyperedge HE-6 (induced by source paper "dual query practical private query release for high dimensional data"):
  with members:
    - "characterizing the sample complexity of private learners"
    - "calibrating data to sensitivity in private data analysis"
    - "an adaptive mechanism for accurate query answering under differential privacy"
    - "faster algorithms for privately releasing marginals"
    - "accurate and efficient private release of datacubes and contingency tables"
    - "answering n 2 o 1 counting queries with differential privacy is hard"
    - "a learning theory approach to non interactive database privacy"
    - "iterative constructions and private data release"

Connected through hyperedge HE-7 (induced by source paper "privately solving linear programs"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"
    - "answering n 2 o 1 counting queries with differential privacy is hard"
    - "iterative constructions and private data release"
    - "a simple and practical algorithm for differentially private data release"
    - "privately releasing conjunctions and the statistical query barrier"
    - "differentially private combinatorial optimization"
    - "interactive privacy via the median mechanism"

Connected through hyperedge HE-8 (induced by source paper "asymptotically truthful equilibrium selection in large congestion games"):
  with members:
    - "private matchings and allocations"
    - "privacy and mechanism design"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"
    - "approximately optimal mechanism design via 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.DS(Data Structures and Algorithms)