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: "spectrum investment under uncertainty a behavioral economics perspective"
  Abstract: In this paper, we study a virtual wireless operator's spectrum investment problem under spectrum supply uncertainty. To obtain enough spectrum resources to meet its customer demands, the virtual operator can either sense for the temporarily unused spectrum in a licensed band, or lease spectrum from a spectrum owner. Sensing is usually cheaper than leasing, but the amount of available spectrum obtained by sensing is uncertain due to the primary users' activities in the licensed band. Previous studies on spectrum investment problems mainly considered the expected profit maximization problem of a risk-neutral operator based on the expected utility theory (EUT). In reality, however, an operator's decision is influenced by not only the consideration of expected profit maximization, but also the level of its risk preference. To capture this tradeoff between these two considerations, we analyze the operator's optimal decision problem using the prospect theory from behavioral economics, which includes EUT as a special case. The sensing and leasing optimal problem under prospect theory is non-convex and challenging to solve. Nevertheless, by exploiting the unimodal structure of the problem, we are able to compute the unique global optimal solution. We show that comparing to an EUT operator, both the risk-averse and risk-seeking operator achieve a smaller expected profit. On the other hand, a risk-averse operator can guarantee a larger minimum possible profit, while a risk-seeking operator can achieve a larger maximum possible profit. Furthermore, the tradeoff between the expected profit and the minimum possible profit for a risk-averse operator is better when the sensing cost increases, while the tradeoff between the expected profit and the maximum possible profit for a risk-seeking operator is better when the sensing cost decreases.

Connected through hyperedge HE-1 (induced by source paper "prospect theoretic analysis of privacy preserving mechanism"):
  with members:
    - "a mean field stackelberg game approach for obfuscation adoption in empirical risk minimization"
    - "managing price uncertainty in prosumer centric energy trading a prospect theoretic stackelberg game approach"
    - "reap an efficient incentive mechanism for reconciling aggregation accuracy and individual privacy in crowdsensing"
    - "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game"
    - "hardware trojan detection game a prospect theoretic approach"
    - "prospect theory for enhanced smart grid resilience using distributed energy storage"
    - "market model and optimal pricing scheme of big data and internet of things iot"
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"

Connected through hyperedge HE-2 (induced by source paper "prospect theory for human centric communications"):
  with members:
    - "does network quality matter a field study of mobile user satisfaction"
    - "mobile data trading behavioral economics analysis and algorithm design"
    - "multicarrier relay selection for full duplex relay assisted ofdm d2d systems"
    - "towards a consumer centric grid a behavioral perspective"
    - "prospect pricing in cognitive radio networks"

Connected through hyperedge HE-3 (induced by source paper "strategic communication between prospect theoretic agents over a gaussian test channel"):
  with members:
    - "information theoretic approach to strategic communication as a hierarchical game"
    - "prospect theory for enhanced cyber physical security of drone delivery systems a network interdiction game"
    - "prospect theory for enhanced smart grid resilience using distributed energy storage"
    - "towards the design of prospect theory based human decision rules for hypothesis testing"

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)