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: "motivating smartphone collaboration in data acquisition and distributed computing"
  Abstract: This paper analyzes and compares different incentive mechanisms for a master to motivate the collaboration of smartphone users on both data acquisition and distributed computing applications. To collect massive sensitive data from users, we propose a reward-based collaboration mechanism, where the master announces a total reward to be shared among collaborators, and the collaboration is successful if there are enough users wanting to collaborate. We show that if the master knows the users' collaboration costs, then he can choose to involve only users with the lowest costs. However, without knowing users' private information, then he needs to offer a larger total reward to attract enough collaborators. Users will benefit from knowing their costs before the data acquisition. Perhaps surprisingly, the master may benefit as the variance of users' cost distribution increases. To utilize smartphones' computation resources to solve complex computing problems, we study how the master can design an optimal contract by specifying different task-reward combinations for different user types. Under complete information, we show that the master involves a user type as long as the master's preference characteristic outweighs that type's unit cost. All collaborators achieve a zero payoff in this case. If the master does not know users' private cost information, however, he will conservatively target at a smaller group of users with small costs, and has to give most benefits to the collaborators.

Connected through hyperedge HE-1 (induced by source paper "a crowdsourcing framework for on device federated learning"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "federated learning over wireless networks convergence analysis and resource allocation"
    - "on device federated learning via blockchain and its latency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "online job scheduling in distributed machine learning clusters"
    - "federated optimization for heterogeneous networks"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "beyond inferring class representatives user level privacy leakage from federated learning"

Connected through hyperedge HE-2 (induced by source paper "regulating competition in age of information under network externalities"):
  with members:
    - "economics of age of information management under network externalities"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "economics of age of information management under network externalities"):
  with members:
    - "how to price fresh data"
    - "dynamic pricing for controlling age of information"
    - "age optimal information updates in multihop networks"
    - "update or wait how to keep your data fresh"
    - "optimizing data freshness throughput and delay in multi server information update systems"
    - "on the age of information in status update systems with packet management"
    - "attack prevention for collaborative spectrum sensing in cognitive radio networks"

Connected through hyperedge HE-4 (induced by source paper "dynamic pricing for controlling age of information"):
  with members:
    - "economics of age of information management under network externalities"
    - "how to price fresh data"
    - "update or wait how to keep your data fresh"

Connected through hyperedge HE-5 (induced by source paper "the economics of video websites with membership advertising mode in wireless networks"):
  with members:
    - "pricing and resource allocation via game theory for a small cell video caching system"
    - "auction based coopetition between lte unlicensed and wi fi"
    - "joint caching and pricing strategies for popular content in information centric networks"
    - "what will 5g be"
    - "five disruptive technology directions for 5g"
    - "price based resource allocation for spectrum sharing femtocell networks a stackelberg game approach"
    - "price differentiation for communication networks"

Connected through hyperedge HE-6 (induced by source paper "volunteers in the smart city comparison of contribution strategies on human centered measures"):
  with members:
    - "privacy enhancing aggregation of internet of things data via sensors grouping"
    - "aspiration learning in coordination games"
    - "evolutionary algorithms for reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "scalable mobile crowdsensing via peer to peer data sharing"):
  with members:
    - "economic analysis of crowdsourced wireless community networks"
    - "dynamic routing for social information sharing"
    - "an integrated spectrum and information market for green cognitive communications"
    - "providing long term participation incentive in participatory sensing"
    - "a game theoretic analysis of user behaviors in crowdsourced wireless community networks"
    - "mine gold to deliver green cognitive communications"
    - "hysim a hybrid spectrum and information market for tv white space networks"
    - "distributed time sensitive task selection in mobile crowdsensing"

Connected through hyperedge HE-8 (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"

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)