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: "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.

Connected through hyperedge HE-1 (induced by source paper "differentially private call auctions and market impact"):
  with members:
    - "differentially private double spectrum auction with approximate social welfare maximization"
    - "strategic classification from revealed preferences"
    - "private matchings and allocations"
    - "inducing approximately optimal flow using truthful mediators"
    - "approximately stable school optimal and student truthful many to one matchings via differential privacy"
    - "privacy and truthful equilibrium selection for aggregative games"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "mechanism design in large games incentives and privacy"

Connected through hyperedge HE-2 (induced by source paper "reviewing and improving the gaussian mechanism for differential privacy"):
  with members:
    - "privacy and utility tradeoff in approximate differential privacy"
    - "the podium mechanism improving on the laplace and staircase mechanisms"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "truncated laplacian mechanism for approximate differential privacy"
    - "private model compression via knowledge distillation"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "knock knock who s there membership inference on aggregate location data"
    - "local differential privacy for physical sensor data and sparse recovery"

Connected through hyperedge HE-3 (induced by source paper "scalable and jointly differentially private packing"):
  with members:
    - "near optimal jointly private packing algorithms via dual multiplicative weight update"
    - "differentially private contextual linear bandits"
    - "private matchings and allocations"
    - "the possibilities and limitations of private prediction markets"
    - "privacy odometers and filters pay as you go composition"
    - "truthful linear regression"
    - "local private efficient protocols for succinct histograms"
    - "coordination complexity small information coordinating large populations"

Connected through hyperedge HE-4 (induced by source paper "near optimal jointly private packing algorithms via dual multiplicative weight update"):
  with members:
    - "private matchings and allocations"
    - "inducing approximately optimal flow using truthful mediators"
    - "privately solving linear programs"
    - "fast algorithms for online stochastic convex programming"
    - "buying private data without verification"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"

Connected through hyperedge HE-5 (induced by source paper "facility location problem in differential privacy model revisited"):
  with members:
    - "scalable and jointly differentially private packing"
    - "near optimal jointly private packing algorithms via dual multiplicative weight update"
    - "on facility location with general lower bounds"
    - "private matchings and allocations"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"
    - "approximately optimal mechanism design via differential privacy"
    - "differentially private combinatorial optimization"

Connected through hyperedge HE-6 (induced by source paper "differential privacy of aggregated dc optimal power flow data"):
  with members:
    - "architecture and algorithms for privacy preserving thermal inertial load management by a load serving entity"
    - "privately solving linear programs"

Connected through hyperedge HE-7 (induced by source paper "adversarial bandits with knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "introduction to online convex optimization"
    - "introduction to multi armed bandits"
    - "bandit convex optimization for scalable and dynamic iot management"
    - "acceleration through optimistic no regret dynamics"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "unifying the stochastic and the adversarial bandits with knapsack"

Connected through hyperedge HE-8 (induced by source paper "learning optimal reserve price against non myopic bidders"):
  with members:
    - "multi scale online learning and its applications to online auctions"
    - "the price of differential privacy for online learning"
    - "learning simple auctions"
    - "the sample complexity of revenue maximization"
    - "sampling and representation complexity of revenue maximization"
    - "making the most of your samples"
    - "perfect bayesian equilibria in repeated sales"
    - "learning prices for repeated auctions with strategic buyers"

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)