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: "spatio temporal pricing for ridesharing platforms"
  Abstract: Ridesharing platforms match drivers and riders to trips, using dynamic prices to balance supply and demand. A challenge is to set prices that are appropriately smooth in space and time, in the sense that drivers will choose to accept their dispatched trips, rather than drive to another area or wait for higher prices or a better trip. We introduce the Spatio-Temporal Pricing (STP) mechanism. The mechanism is incentive-aligned, in that it is a subgame-perfect equilibrium for drivers to accept their dispatches, and the mechanism is welfare-optimal, envy-free, individually rational and budget balanced from any history onward. We work in a complete information, discrete time, multi-period, multi-location model, and prove the existence of anonymous, origin-destination, competitive equilibrium (CE) prices. The STP mechanism employs driver-pessimal CE prices, and the proof of incentive alignment makes use of the $M^\natural$ concavity of min-cost flow objectives. The same connection to min-cost flow problems provides an efficient algorithm to compute an optimal matching and prices. We also give an impossibility result, that there can be no dominant-strategy mechanism with the same economic properties. An empirical analysis conducted in simulation suggests that the STP mechanism can achieve significantly higher social welfare than a myopic pricing mechanism, and highlights the failure of incentive alignment due to non-smooth prices in myopic mechanisms.

Connected through hyperedge HE-1 (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"
    - "stochastic k server how should uber work"
    - "fusible hsts and the randomized k server conjecture"

Connected through hyperedge HE-2 (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"
    - "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"
    - "pricing and optimization in shared vehicle systems an approximation framework"
    - "rapidly mixing markov chains a comparison of techniques a survey"
    - "an optimization framework for online ride sharing markets"

Connected through hyperedge HE-3 (induced by source paper "balancing the tradeoff between profit and fairness in rideshare platforms during high demand hours"):
  with members:
    - "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"
    - "pricing and optimization in shared vehicle systems an approximation framework"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

Connected through hyperedge HE-4 (induced by source paper "analyzing location based advertising for vehicle service providers using effective resistances"):
  with members:
    - "prices and subsidies in the sharing economy"
    - "pricing and optimization in shared vehicle systems an approximation framework"
    - "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-5 (induced by source paper "dynamic trip vehicle dispatch with scheduled and on demand requests"):
  with members:
    - "flow equilibria via online surge pricing"
    - "optimal vehicle dispatching schemes via dynamic pricing"
    - "taxi dispatch with real time sensing data in metropolitan areas a receding horizon control approach"

Connected through hyperedge HE-6 (induced by source paper "driver surge pricing"):
  with members:
    - "maximizing efficiency in dynamic matching markets"
    - "mean field equilibria for resource competition in spatial settings"

Connected through hyperedge HE-7 (induced by source paper "ridesharing with driver location preferences"):
  with members:
    - "pricing and optimization in shared vehicle systems an approximation framework"

Connected through hyperedge HE-8 (induced by source paper "flow equilibria via online surge pricing"):
  with members:
    - "pricing online decisions beyond auctions"

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)