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: "dynamic routing for social information sharing"
  Abstract: Today mobile users are intensively interconnected thanks to the emerging mobile social networks, where they share location-based information with each other when traveling on different routes and visit different areas of the city. In our model the information collected is aggregated over all users' trips and made publicly available as a public good. Due to information overlap, the total useful content amount increases with the diversity in path choices made by the users, and it is crucial to motivate selfish users to choose different paths despite the potentially higher costs associated with their trips. In this paper we combine the benefits from social information sharing with the fundamental routing problem where a unit mass of non-atomic selfish users decide their trips in a non-cooperative game by choosing between a high-cost and a low-cost path. To remedy the inefficient low-content equilibrium where all users choose to explore a single path (the low-cost path), we propose and analyse two new incentive mechanisms that can be used by the social network application, one based on side payments and the other on restricting access to content for users that choose the low cost path. We also obtain interesting price of anarchy results that show some fundamental tradeoffs between achieving path diversity and maintaining greater user participation, motivating a combined mechanism to further increase the social welfare. Our model extends classical dynamic routing in the case of externalities caused from traffic on different paths of the network.

Connected through hyperedge HE-1 (induced by source paper "crowd mecs a novel crowdsourcing framework for mobile edge caching and sharing"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "recommending paths follow or not follow"
    - "high throughput opportunistic cooperative device to device communications with caching"

Connected through hyperedge HE-2 (induced by source paper "optimal dynamic information provision in traffic routing"):
  with members:
    - "bandit learning with positive externalities"
    - "effects of information heterogeneity in bayesian routing games"
    - "informational braess paradox the effect of information on traffic congestion"
    - "matching while learning"

Connected through hyperedge HE-3 (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"
    - "the age of information real time status updating by multiple sources"

Connected through hyperedge HE-4 (induced by source paper "a stackelberg game approach towards socially aware incentive mechanisms for mobile crowdsensing online report"):
  with members:
    - "a socially aware incentive mechanism for mobile crowdsensing service market"
    - "crowdsourcing with tullock contests a new perspective"
    - "optimal pricing in networks with externalities"
    - "optimal pricing in social networks with incomplete information"

Connected through hyperedge HE-5 (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"
    - "scalable mobile crowdsensing via peer to peer data sharing"
    - "optimal pricing in networks with externalities"

Connected through hyperedge HE-6 (induced by source paper "scalable mobile crowdsensing via peer to peer data sharing"):
  with members:
    - "economic analysis of crowdsourced wireless community networks"
    - "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"
    - "combining spot and futures markets a hybrid market approach to dynamic spectrum access"

Connected through hyperedge HE-7 (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"
    - "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"

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)