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 "economics of age of information management under network externalities"):
  Abstract: Online content platforms concern about the freshness of their content updates to their end customers, and increasingly more platforms now invite and pay the crowd to share real-time information (e.g., news and sensor data) to help reduce their ages of information (AoI). How much crowdsensed 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 two platforms coexist in sharing the content delivery network of limited bandwidth, and one platform's update may jam or preempt the other's under negative network externalities. When the two selfish platforms know each other's sampling costs, we formulate their interaction as a non-cooperative game and show both want to over-sample to reduce their own AoI, causing the price of anarchy (PoA) to be infinity. To remedy this huge efficiency loss, we propose a non-monetary trigger mechanism of punishment in a repeated game to enforce the platforms' cooperation to achieve the social optimum. We also study the more challenging incomplete information scenario that platform 1 knows more information about sampling cost than platform 2 by hiding its sampling cost information in the Bayesian game. Perhaps surprisingly, we show that even platform 1 may get hurt by knowing more information. We successfully redesign the trigger-and-punishment mechanism to negate platform 1's information advantage and ensure no cheating. As compared to the social optimum, extensive simulations show that the mechanisms can remedy the huge efficiency loss due to platform competition in different information scenarios.

Includes members:
  Member "how to price fresh data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "how to price fresh data")
  Member "age optimal information updates in multihop networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "timely updates by multiple sources the m m 1 queue revisited")
  Member "update or wait how to keep your data fresh":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "who should google scholar update more often")
  Member "optimizing data freshness throughput and delay in multi server information update systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "who should google scholar update more often")
  Member "on the age of information in status update systems with packet management":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "age of information with gilbert elliot servers and samplers")
  Member "motivating smartphone collaboration in data acquisition and distributed computing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a crowdsourcing framework for on device federated learning")
  Member "attack prevention for collaborative spectrum sensing in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "regulating competition in age of information under network externalities")

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)