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: "computationally feasible vcg mechanisms"
  Abstract: A major achievement of mechanism design theory is a general method for the construction of truthful mechanisms called VCG (Vickrey, Clarke, Groves). When applying this method to complex problems such as combinatorial auctions, a difficulty arises: VCG mechanisms are required to compute optimal outcomes and are, therefore, computationally infeasible. However, if the optimal outcome is replaced by the results of a sub-optimal algorithm, the resulting mechanism (termed VCG-based) is no longer necessarily truthful. The first part of this paper studies this phenomenon in depth and shows that it is near universal. Specifically, we prove that essentially all reasonable approximations or heuristics for combinatorial auctions as well as a wide class of cost minimization problems yield non-truthful VCG-based mechanisms. We generalize these results for affine maximizers.#R##N##R##N#The second part of this paper proposes a general method for circumventing the above problem. We introduce a modification of VCG-based mechanisms in which the agents are given a chance to improve the output of the underlying algorithm. When the agents behave truthfully, the welfare obtained by the mechanism is at least as good as the one obtained by the algorithm's output. We provide a strong rationale for truth-telling behavior. Our method satisfies individual rationality as well.

Connected through hyperedge HE-1 (induced by source paper "machine learning powered iterative combinatorial auctions"):
  with members:
    - "fast iterative combinatorial auctions via bayesian learning"
    - "a bayesian clearing mechanism for combinatorial auctions"
    - "a general theory of sample complexity for multi item profit maximization"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "learning simple auctions"
    - "sample complexity of automated mechanism design"
    - "the sample complexity of revenue maximization"
    - "ironing in the dark"

Connected through hyperedge HE-2 (induced by source paper "online learning for measuring incentive compatibility in ad auctions"):
  with members:
    - "learning to bid without knowing your value"
    - "multiplicative pacing equilibria in auction markets"
    - "combinatorial multi armed bandit with general reward functions"
    - "online learning in repeated auctions"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "online learning and profit maximization from revealed preferences"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "mechanism design for data science"

Connected through hyperedge HE-3 (induced by source paper "wireless network slicing generalized kelly mechanism based resource allocation"):
  with members:
    - "common throughput maximization in uav enabled ofdma systems with delay consideration"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "applications of economic and pricing models for wireless network security a survey"
    - "virtualization of 5g cellular networks as a hierarchical combinatorial auction"
    - "a dynamic embedding algorithm for wireless network virtualization"
    - "composable and efficient mechanisms"

Connected through hyperedge HE-4 (induced by source paper "efficient multi resource multi unit vcg auction"):
  with members:
    - "non monetary fair scheduling a cooperative game theory approach"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "fair allocation without trade"
    - "no justified complaints on fair sharing of multiple resources"
    - "optimal solutions for multi unit combinatorial auctions branch and bound heuristics"

Connected through hyperedge HE-5 (induced by source paper "assignment and pricing of shared rides in ride sourcing using combinatorial double auctions"):
  with members:
    - "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
    - "combinatorial auction based pricing for multi tenant autonomous vehicle public transportation system"

Connected through hyperedge HE-6 (induced by source paper "the pareto frontier of inefficiency in mechanism design"):
  with members:
    - "multiagent maximum coverage problems the trade off between anarchy and stability"
    - "correlated and coarse equilibria of single item auctions"
    - "the anarchy of scheduling without money"
    - "the price of anarchy in auctions"
    - "setting lower bounds on truthfulness"
    - "the vcg mechanism for bayesian scheduling"
    - "social welfare in one sided matching mechanisms"
    - "bayesian truthful mechanisms for job scheduling from bi criterion approximation algorithms"

Connected through hyperedge HE-7 (induced by source paper "data centric mobile crowdsensing"):
  with members:
    - "scalable mobile crowdsensing via peer to peer data sharing"
    - "two sided matching based cooperative spectrum sharing"
    - "economic analysis of crowdsourced wireless community networks"
    - "centurion incentivizing multi requester mobile crowd sensing"
    - "dynamic routing for social information sharing"
    - "providing long term participation incentive in participatory sensing"
    - "distributed time sensitive task selection in mobile crowdsensing"
    - "combining spot and futures markets a hybrid market approach to dynamic spectrum access"

Connected through hyperedge HE-8 (induced by source paper "a double auction mechanism for mobile crowd sensing with data reuse"):
  with members:
    - "providing long term participation incentive in participatory sensing"
    - "combining spot and futures markets a hybrid market approach to dynamic spectrum access"
    - "fairness and social welfare in incentivizing participatory sensing"

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)