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: "privacy and mechanism design"
  Abstract: This paper is a survey of recent work at the intersection of mechanism design and privacy. The connection is a natural one, but its study has been jump-started in recent years by the advent of differential privacy, which provides a rigorous, quantitative way of reasoning about the costs that an agent might experience because of the loss of his privacy. Here, we survey several facets of this study, and differential privacy plays a role in more than one way. Of course, it provides us a basis for modeling agent costs for privacy, which is essential if we are to attempt mechanism design in a setting in which agents have preferences for privacy. It also provides a toolkit for controlling those costs. However, perhaps more surprisingly, it provides a powerful toolkit for controlling the stability of mechanisms in general, which yields a set of tools for designing novel mechanisms even in economic settings completely unrelated to privacy.

Connected through hyperedge HE-1 (induced by source paper "segmentation incentives and privacy"):
  with members:
    - "the strange case of privacy in equilibrium models"
    - "calibrating data to sensitivity in private data analysis"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"
    - "privacy aware mechanism design"
    - "approximately optimal mechanism design via differential privacy"

Connected through hyperedge HE-2 (induced by source paper "reap an efficient incentive mechanism for reconciling aggregation accuracy and individual privacy in crowdsensing"):
  with members:
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"
    - "crowdsourcing with tullock contests a new perspective"
    - "distributed time sensitive task selection in mobile crowdsensing"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "calibrating data to sensitivity in private data analysis"
    - "take it or leave it running a survey when privacy comes at a cost"
    - "approximately optimal auctions for selling privacy when costs are correlated with data"
    - "selling privacy at auction"

Connected through hyperedge HE-3 (induced by source paper "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"):
  with members:
    - "putting peer prediction under the micro economic scope and making truth telling focal"
    - "privacy games optimal user centric data obfuscation"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "local private efficient protocols for succinct histograms"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "buying private data without verification"
    - "on the relation between identifiability differential privacy and mutual information privacy"

Connected through hyperedge HE-4 (induced by source paper "private matchings and allocations"):
  with members:
    - "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"
    - "differentially private combinatorial optimization"

Connected through hyperedge HE-5 (induced by source paper "belief invariant equilibria in games with incomplete information"):
  with members:
    - "the strange case of privacy in equilibrium models"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-6 (induced by source paper "higher order approximate relational refinement types for mechanism design and differential privacy"):
  with members:
    - "dual query practical private query release for high dimensional data"
    - "privacy and truthful equilibrium selection for aggregative games"
    - "calibrating data to sensitivity in private data analysis"
    - "on the power of randomization in algorithmic mechanism design"

Connected through hyperedge HE-7 (induced by source paper "privacy and truthful equilibrium selection for aggregative games"):
  with members:
    - "private matchings and allocations"
    - "finding any nontrivial coarse correlated equilibrium is hard"
    - "privately solving linear programs"
    - "on the complexity of nash equilibria in anonymous games"
    - "privacy preserving public information for sequential games"
    - "approximately stable school optimal and student truthful many to one matchings via differential privacy"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-8 (induced by source paper "redrawing the boundaries on purchasing data from privacy sensitive individuals"):
  with members:
    - "private learning and sanitization pure vs approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "conducting truthful surveys cheaply"
    - "mechanism design in large games incentives and privacy"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"
    - "take it or leave it running a survey when privacy comes at a cost"
    - "approximately optimal auctions for selling privacy when costs are correlated with data"
    - "truthful mechanisms for agents that value 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.GT(Computer Science and Game Theory)