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: "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.

Connected through hyperedge HE-1 (induced by source paper "age of information for multicast transmission with fixed and random deadlines in iot systems"):
  with members:
    - "age of information for computation intensive messages in mobile edge computing"
    - "average peak age of information minimization in uav assisted iot networks"
    - "minimizing the age of information in wireless networks with stochastic arrivals"
    - "broadcast age of information in csma ca based wireless networks"
    - "adaptive coding for information freshness in a two user broadcast erasure channel"
    - "scheduling policies for minimizing age of information in broadcast wireless networks"
    - "age minimal online policies for energy harvesting sensors with random battery recharges"
    - "reinforcement learning based scheduling algorithm for optimizing age of information in ultra reliable low latency networks"

Connected through hyperedge HE-2 (induced by source paper "how to price fresh data"):
  with members:
    - "timely updates in energy harvesting two hop networks offline and online policies"
    - "dynamic pricing for controlling age of information"
    - "a dynamic jamming game for real time status updates"
    - "joint status sampling and updating for minimizing age of information in the internet of things"
    - "optimizing information freshness in wireless networks under general interference constraints"
    - "on the role of age of information in internet of things"
    - "age minimal transmission for energy harvesting sensors with finite batteries online policies"
    - "optimal status update for age of information minimization with an energy harvesting source"

Connected through hyperedge HE-3 (induced by source paper "regulating competition in age of information under network externalities"):
  with members:
    - "how to price fresh data"
    - "dynamic pricing for controlling age of information"
    - "scalable mobile crowdsensing via peer to peer data sharing"
    - "age optimal information updates in multihop networks"
    - "minimizing the age of the information through queues"
    - "update or wait how to keep your data fresh"
    - "dynamic routing for social information sharing"
    - "the age of information real time status updating by multiple sources"

Connected through hyperedge HE-4 (induced by source paper "dynamic pricing for controlling age of information"):
  with members:
    - "how to price fresh data"
    - "update or wait how to keep your data fresh"
    - "motivating smartphone collaboration in data acquisition and distributed computing"

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)