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 "regulating competition in age of information under network externalities"):
  Abstract: Online content platforms are concerned about the freshness of their content updates to their end customers, and increasingly more platforms now invite and pay the crowd to sample real-time information (e.g., traffic observations and sensor data) to help reduce their ages of information (AoI). How much crowdsourced data to sample and buy over time is a critical question for a platform's AoI management, requiring a good balance between its AoI and the incurred sampling cost. This question becomes more interesting by considering the stage after sampling, where multiple platforms coexist in sharing the content delivery network of limited bandwidth, and one platform's update may jam or preempt the others' under negative network externalities. When these selfish platforms know each other's sampling cost, we formulate their competition as a non-cooperative game and show they want to over-sample to reduce their own AoIs, causing the price of anarchy (PoA) to be infinity. To remedy this huge efficiency loss, we propose a trigger mechanism of non-monetary punishment in a repeated game to enforce the platforms' cooperation to approach the social optimum. We also study the more challenging scenario of incomplete information that some new platform hides its private sampling cost information from the other incumbent platforms in the Bayesian game. Perhaps surprisingly, we show that even the platform with more information may get hurt. We successfully redesign the trigger-and-punishment mechanism to negate the platform's information advantage and ensure no cheating. Our extensive simulations show that the mechanisms can remedy the huge efficiency loss due to platform competition, and the performance improves as we have more incumbent platforms with known cost information.

Includes members:
  Member "economics of age of information management under network externalities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "age of information for multicast transmission with fixed and random deadlines in iot systems")
  Member "how to price fresh data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "timely cloud computing preemption and waiting")
  Member "dynamic pricing for controlling age of information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how to price fresh data")
  Member "scalable mobile crowdsensing via peer to peer data sharing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal pricing of internet of things a machine learning approach")
  Member "age optimal information updates in multihop networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "timely updates by multiple sources the m m 1 queue revisited")
  Member "minimizing the age of the information through queues":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "waiting before serving a companion to packet management in status update systems")
  Member "update or wait how to keep your data fresh":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "who should google scholar update more often")
  Member "dynamic routing for social information sharing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "crowd mecs a novel crowdsourcing framework for mobile edge caching and sharing")

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.GT(Computer Science and Game Theory)