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: "investment and pricing with spectrum uncertainty a cognitive operator s perspective"
  Abstract: This paper studies the optimal investment and pricing decisions of a cognitive mobile virtual network operator (C-MVNO) under spectrum supply uncertainty. Compared with a traditional MVNO who often leases spectrum via long-term contracts, a C-MVNO can acquire spectrum dynamically in short-term by both sensing the empty “spectrum holes” of licensed bands and dynamically leasing from the spectrum owner. As a result, a C-MVNO can make flexible investment and pricing decisions to match demands of the secondary unlicensed users. Compared to dynamic spectrum leasing, spectrum sensing is typically cheaper, but the obtained useful spectrum amount is random due to primary licensed users' stochastic traffic. The C-MVNO needs to determine the optimal amounts of spectrum sensing and leasing by evaluating the trade-off between cost and uncertainty. The C-MVNO also needs to determine the optimal price to sell the spectrum to the secondary unlicensed users, taking into account wireless heterogeneity of users such as different maximum transmission power levels and channel gains. We model and analyze the interactions between the C-MVNO and secondary unlicensed users as a Stackelberg game. We show several interesting properties of the network equilibrium, including threshold structures of the optimal investment and pricing decisions, the independence of the optimal price on users' wireless characteristics, and guaranteed fair and predictable QoS among users. We prove that these properties hold for general SNR regime and general continuous distributions of sensing uncertainty. We show that spectrum sensing can significantly improve the C-MVNO's expected profit and users' payoffs.

Connected through hyperedge HE-1 (induced by source paper "the interplay of competition and cooperation among service providers part ii"):
  with members:
    - "the economics of competition and cooperation between mnos and mvnos"
    - "spectrum leasing and cooperative resource allocation in cognitive ofdma networks"
    - "spatial sinr games combining base station placement and mobile association"
    - "spectrum sharing between wireless networks"

Connected through hyperedge HE-2 (induced by source paper "distributed resource allocation for network slicing over licensed and unlicensed bands"):
  with members:
    - "network slicing games enabling customization in multi tenant networks"
    - "coordination protocol for inter operator spectrum sharing in co primary 5g small cell networks"
    - "sharing of unlicensed spectrum by strategic operators"
    - "joint energy and spectrum cooperation for cellular communication systems"
    - "cooperative games with overlapping coalitions"

Connected through hyperedge HE-3 (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-4 (induced by source paper "spectrum investment under uncertainty a behavioral economics perspective"):
  with members:
    - "prospect pricing in cognitive radio networks"
    - "understanding mobile traffic patterns of large scale cellular towers in urban environment"
    - "integrating energy storage into the smart grid a prospect theoretic approach"
    - "dynamic profit maximization of cognitive mobile virtual network operator"
    - "evolutionarily stable spectrum access"

Connected through hyperedge HE-5 (induced by source paper "an integrated spectrum and information market for green cognitive communications"):
  with members:
    - "cooperative wi fi deployment a one to many bargaining framework"
    - "mine gold to deliver green cognitive communications"
    - "hysim a hybrid spectrum and information market for tv white space networks"
    - "bargaining based mobile data offloading"

Connected through hyperedge HE-6 (induced by source paper "optimal pricing and admission control for heterogeneous secondary users"):
  with members:
    - "time and location aware mobile data pricing"
    - "mine gold to deliver green cognitive communications"
    - "a hybrid pricing framework for tv white space database"

Connected through hyperedge HE-7 (induced by source paper "dynamic spectrum leasing with two sellers"):
  with members:
    - "uncertain price competition in a duopoly with heterogeneous availability"
    - "dynamic profit maximization of cognitive mobile virtual network operator"

Connected through hyperedge HE-8 (induced by source paper "imitation based social spectrum sharing"):
  with members:
    - "database assisted distributed spectrum sharing"
    - "dynamic profit maximization of cognitive mobile virtual network operator"
    - "let cognitive radios imitate imitation based spectrum access for cognitive radio networks"
    - "adaptive channel allocation spectrum etiquette for cognitive radio networks"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

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.NI(Networking and Internet Architecture)