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 "jointly private convex programming"):
  Abstract: We present a general method for approximately solving convex programs defined by private information from agents, when the solution can be naturally partitioned among the agents. This class of problems includes multi-commodity flow problems, general allocation problems, and multi-dimensional knapsack problems, among other examples. The accuracy of our algorithm depends on the number of coupling constraints, which bind multiple agents. On the other hand, our accuracy is nearly independent of the number of variables, and in many cases, actually improves as the number of agents increases. A special case of our result (solving general allocation problems beyond "Gross Substitute" preferences) resolves the main open problem from [Hsu et al. STOC 2014].   We also consider strategic agents who have preferences over their part of the solution. For any convex program in our class that maximizes social welfare, we show how to create an approximately dominant strategy truthful mechanism, approximately maximizing welfare. The central idea is to charge agents prices based on the approximately optimal dual variables, which are themselves computed under differential privacy. Our results substantially broaden the class of problems that are known to be solvable under privacy and/or incentive constraints.

Includes members:
  Member "private matchings and allocations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differentially private call auctions and market impact")
  Member "privately solving linear programs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient noise tolerant and private learning via boosting")
  Member "differentially private empirical risk minimization efficient algorithms and tight error bounds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentially private deep learning with smooth sensitivity")
  Member "privacy and truthful equilibrium selection for aggregative games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differentially private call auctions and market impact")
  Member "differential privacy for the analyst via private equilibrium computation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient noise tolerant and private learning via boosting")
  Member "asymptotically truthful equilibrium selection in large congestion games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "differentially private call auctions and market impact")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "locally private hypothesis selection")
  Member "mechanism design in large games incentives and privacy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "differentially private call auctions and market impact")

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)