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: "target tracking via crowdsourcing a mechanism design approach"
  Abstract: In this paper, we propose a crowdsourcing-based framework for myopic target tracking by designing an optimal incentive-compatible mechanism in a wireless sensor network (WSN) containing sensors that are selfish and profit-motivated. In typical WSNs which have limited bandwidth, the fusion center (FC) has to distribute the total number of bits that can be transmitted from the sensors to the FC among the sensors. In the formulation considered here, the FC conducts an auction by soliciting bids from the selfish sensors, which reflect how much they value their energy cost. Furthermore, the rationality and truthfulness of the sensors are guaranteed in our model. The final problem is formulated as a multiple-choice knapsack problem (MCKP), which is solved by the dynamic programming method in pseudo-polynomial time. Simulation results show the effectiveness of our proposed approach in terms of both the tracking performance and lifetime of the sensor network.

Connected through hyperedge HE-1 (induced by source paper "mobile edge caching an optimal auction approach"):
  with members:
    - "fundamental tradeoff between storage and latency in cache aided wireless interference networks"
    - "how much cache is needed to achieve linear capacity scaling in backhaul limited dense wireless networks"
    - "pricing and resource allocation via game theory for a small cell video caching system"
    - "information theoretic caching the multi user case"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "joint caching and pricing strategies for popular content in information centric networks"
    - "wireless content caching for small cell and d2d networks"
    - "a rate distortion approach to caching"

Connected through hyperedge HE-2 (induced by source paper "profit maximization auction and data management in big data markets"):
  with members:
    - "market model and optimal pricing scheme of big data and internet of things iot"

Connected through hyperedge HE-3 (induced by source paper "data collection and wireless communication in internet of things iot using economic analysis and pricing models a survey"):
  with members:
    - "economics of internet of things iot an information market approach"
    - "non stationary resource allocation policies for delay constrained video streaming application to video over internet of things enabled networks"
    - "from participatory sensing to mobile crowd sensing"
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "motivating smartphone collaboration in data acquisition and distributed computing"
    - "fairness and social welfare in incentivizing participatory sensing"
    - "optimal pricing and load sharing for energy saving in communications cooperation"
    - "incentives for privacy tradeoff in community sensing"

Connected through hyperedge HE-4 (induced by source paper "economics of internet of things iot an information market approach"):
  with members:
    - "non stationary resource allocation policies for delay constrained video streaming application to video over internet of things enabled networks"

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.SY(Systems and Control)