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: "pricing and optimization in shared vehicle systems an approximation framework"
  Abstract: Optimizing shared vehicle systems (bike-sharing/car-sharing/ride-sharing) is more challenging compared to traditional resource allocation settings due to the presence of \emph{complex network externalities} -- changes in the demand/supply at any location affect future supply throughout the system within short timescales. These externalities are well captured by steady-state Markovian models, which are therefore widely used to analyze such systems. However, using such models to design pricing/control policies is computationally difficult since the resulting optimization problems are high-dimensional and non-convex. #R##N#To this end, we develop a general approximation framework for designing pricing policies in shared vehicle systems, based on a novel convex relaxation which we term \emph{elevated flow relaxation}. Our approach provides the first efficient algorithms with \emph{rigorous approximation guarantees} for a wide range of objective functions (throughput, revenue, welfare). For any shared vehicle system with $n$ stations and $m$ vehicles, our framework provides a pricing policy with an approximation ratio of $1+(n-1)/m$. This guarantee is particularly meaningful when $m/n$, the average number of vehicles per station is large, as is often the case in practice. #R##N#Further, the simplicity of our approach allows us to extend it to more complex settings: rebalancing empty vehicles, redirecting riders to nearby vehicles, multi-objective settings (such as Ramsey pricing), incorporating travel-times, etc. Our approach yields efficient algorithms with the same approximation guarantees for all these problems, and in the process, obtains as special cases several existing heuristics and asymptotic guarantees.

Connected through hyperedge HE-1 (induced by source paper "throughput optimal routing in blockchain based payment systems"):
  with members:
    - "atomic cross chain swaps"
    - "high throughput cryptocurrency routing in payment channel networks"
    - "settling payments fast and private efficient decentralized routing for path based transactions"
    - "privacy preserving shortest path computation"
    - "online advertisement optimization and stochastic networks"

Connected through hyperedge HE-2 (induced by source paper "putting ridesharing to the test efficient and scalable solutions and the power of dynamic vehicle relocation"):
  with members:
    - "shared autonomous vehicle simulation and service design"
    - "dynamic trip vehicle dispatch with scheduled and on demand requests"
    - "optimization of ride sharing systems using event driven receding horizon control"
    - "anytime heuristic for weighted matching through altruism inspired behavior"
    - "the online k taxi problem"
    - "a primal dual online deterministic algorithm for matching with delays"
    - "spatio temporal pricing for ridesharing platforms"
    - "stochastic k server how should uber work"

Connected through hyperedge HE-3 (induced by source paper "mix and match markov chains mixing times for matching in rideshare"):
  with members:
    - "minimizing latency in online ride and delivery services"
    - "spatio temporal pricing for ridesharing platforms"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "deep reinforcement learning an overview"
    - "allocation problems in ride sharing platforms online matching with offline reusable resources"
    - "rapidly mixing markov chains a comparison of techniques a survey"
    - "an optimization framework for online ride sharing markets"

Connected through hyperedge HE-4 (induced by source paper "balancing the tradeoff between profit and fairness in rideshare platforms during high demand hours"):
  with members:
    - "spatio temporal pricing for ridesharing platforms"
    - "attenuate locally win globally an attenuation based framework for online stochastic matching with timeouts"
    - "fair allocation of indivisible public goods"
    - "allocation problems in ride sharing platforms online matching with offline reusable resources"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

Connected through hyperedge HE-5 (induced by source paper "analyzing location based advertising for vehicle service providers using effective resistances"):
  with members:
    - "spatio temporal pricing for ridesharing platforms"
    - "prices and subsidies in the sharing economy"
    - "public wi fi monetization via advertising"
    - "effective and efficient approximations of the generalized inverse of the graph laplacian matrix with an application to current flow betweenness centrality"

Connected through hyperedge HE-6 (induced by source paper "static pricing universal guarantees for reusable resources"):
  with members:
    - "dynamic pricing under a static calendar"

Connected through hyperedge HE-7 (induced by source paper "ridesharing with driver location preferences"):
  with members:
    - "spatio temporal pricing for ridesharing platforms"

Connected through hyperedge HE-8 (induced by source paper "loyalty programs in the sharing economy optimality and competition"):
  with members:
    - "prices and subsidies in the sharing economy"
    - "who benefits from the sharing economy of airbnb"
    - "market share analysis with brand effect"

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)