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: "scalable mobile crowdsensing via peer to peer data sharing"
  Abstract: Mobile crowdsensing (MCS) is a new paradigm of sensing by taking advantage of the rich embedded sensors of mobile user devices. However, the traditional server-client MCS architecture often suffers from the high operational cost on the centralized server (e.g., for storing and processing massive data), hence the poor scalability. Peer-to-peer (P2P) data sharing can effectively reduce the server's cost by leveraging the user devices' computation and storage resources. In this work, we propose a novel P2P-based MCS architecture, where the sensing data is saved and processed in user devices locally and shared among users in a P2P manner. To provide necessary incentives for users in such a system, we propose a quality-aware data sharing market, where the users who sense data can sell data to others who request data but not want to sense the data by themselves. We analyze the user behavior dynamics from the game-theoretic perspective, and characterize the existence and uniqueness of the game equilibrium. We further propose best response iterative algorithms to reach the equilibrium with provable convergence. Our simulations show that the P2P data sharing can greatly improve the social welfare, especially in the model with a high transmission cost and a low trading price.

Connected through hyperedge HE-1 (induced by source paper "optimal pricing of internet of things a machine learning approach"):
  with members:
    - "hybrid pricing for mobile collaborative internet access"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "mobile big data analytics using deep learning and apache spark"
    - "context aware computing for the internet of things a survey"
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "a theory of pricing private data"
    - "a survey of smart data pricing past proposals current plans and future trends"

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"
    - "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-3 (induced by source paper "optimal pricing for a peer to peer sharing platform under network externalities"):
  with members:
    - "mobile social services with network externality from separate pricing to bundled pricing"
    - "dynamic routing for social information sharing"
    - "optimal pricing in networks with externalities"

Connected through hyperedge HE-4 (induced by source paper "network coding with random packet index assignment for data collection networks"):
  with members:
    - "network coding based evolutionary network formation for dynamic wireless networks"
    - "minimal header overhead for random linear network coding"
    - "improved compression of network coding vectors using erasure decoding and list decoding"

Connected through hyperedge HE-5 (induced by source paper "data centric mobile crowdsensing"):
  with members:
    - "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"
    - "motivating smartphone collaboration in data acquisition and distributed computing"

Connected through hyperedge HE-6 (induced by source paper "technical report for user centric participatory sensing a game theoretic analysis"):
  with members:
    - "a double auction mechanism for mobile crowd sensing with data reuse"
    - "mine gold to deliver green cognitive communications"
    - "distributed time sensitive task selection in mobile crowdsensing"

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)