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: "private matchings and allocations"
  Abstract: We consider a private variant of the classical allocation problem: given $k$ goods and $n$ agents with private valuation functions over bundles of goods, how can we allocate goods to agents to maximize social welfare? An important special case is when agents desire at most one good, and specify their (private) value for each good: in this case, the problem is exactly the maximum-weight matching problem in a bipartite graph. Private matching and allocation problems have not been considered in the differential privacy literature for a good reason: they are plainly impossible to solve under differential privacy. Informally, the allocation must match agents to their preferred goods in order to maximize social welfare, but this preference is exactly what agents wish to hide! Therefore, we consider the problem under the relaxed constraint of joint differential privacy: for any agent $i$, no coalition of agents excluding $i$ should be able to learn about the valuation function of agent $i$. In this setting, the ...

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"
    - "jointly private convex programming"
    - "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 "scalable and jointly differentially private packing"):
  with members:
    - "near optimal jointly private packing algorithms via dual multiplicative weight update"
    - "differentially private contextual linear bandits"
    - "the possibilities and limitations of private prediction markets"
    - "privacy odometers and filters pay as you go composition"
    - "jointly private convex programming"
    - "truthful linear regression"
    - "local private efficient protocols for succinct histograms"
    - "coordination complexity small information coordinating large populations"

Connected through hyperedge HE-3 (induced by source paper "near optimal jointly private packing algorithms via dual multiplicative weight update"):
  with members:
    - "jointly private convex programming"
    - "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-4 (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"
    - "jointly private convex programming"
    - "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-5 (induced by source paper "optimal truthful and private securities lending"):
  with members:
    - "censored exploration and the dark pool problem"
    - "mechanism design in large games incentives and privacy"
    - "selling privacy at auction"

Connected through hyperedge HE-6 (induced by source paper "technical privacy metrics a systematic survey"):
  with members:
    - "evaluating the strength of genomic privacy metrics"
    - "constructing elastic distinguishability metrics for location privacy"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "from t closeness to differential privacy and vice versa in data anonymization"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "differential privacy an economic method for choosing epsilon"
    - "utility privacy tradeoffs in databases an information theoretic approach"

Connected through hyperedge HE-7 (induced by source paper "private incremental regression"):
  with members:
    - "private approximations of the 2nd moment matrix using existing techniques in linear regression"
    - "sketching as a tool for numerical linear algebra"
    - "randomness efficient fast johnson lindenstrauss transform with applications in differential privacy and compressed sensing"
    - "private empirical risk minimization beyond the worst case the effect of the constraint set geometry"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "circulant matrices and differential privacy"
    - "private multiplicative weights beyond linear queries"
    - "privacy via the johnson lindenstrauss transform"

Connected through hyperedge HE-8 (induced by source paper "jointly private convex programming"):
  with members:
    - "privately solving linear programs"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "privacy and truthful equilibrium selection for aggregative games"
    - "differential privacy for the analyst via private equilibrium computation"
    - "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"

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.GT(Computer Science and Game Theory)