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: "balancing cost and dissatisfaction in online ev charging under real time pricing"
  Abstract: We consider an increasingly popular demand-response scenario where a user schedules the flexible electric vehicle (EV) charging load in response to real-time electricity prices. The objective is to minimize the total charging cost with user dissatisfaction taken into account. We focus on the online setting where neither accurate prediction nor distribution of future real-time prices is available to the user when making irrevocable charging decision in each time slot. The emphasis on considering user dissatisfaction and achieving optimal competitive ratio differentiates our work from existing ones and makes our study uniquely challenging. Our key contribution is two simple online algorithms with the best possible competitive ratio among all deterministic algorithms. The optimal competitive ratio is upper-bounded by \min\left\{ \sqrt{\alpha/p_{\min}},p_{\max}/p_{\min}\right\} and the bound is asymptotically tight with respect to \alpha, where p_{\max} and p_{\min} are the upper and lower bounds of real-time prices and \alpha\geq p_{\min} captures the consideration of user dissatisfaction. The bound under small and large values of \alpha suggests the fundamental difference of the problems with considering user dissatisfaction (\alpha takes small values) and without (\alpha takes large values). Along the way we also develop a general technique for designing online algorithms from an idea hinted in One-Way-Trading Problem, which can be of independent interest. Simulation results based on real-world traces corroborate our theoretical findings and show that our algorithms achieve substantial performance gain as compared to conceivable alternatives. The results also suggest that increasing EV charging rate limit decreases overall cost almost linearly.

Connected through hyperedge HE-1 (induced by source paper "competitive online optimization under inventory constraints"):
  with members:
    - "nested convex bodies are chaseable"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "online offering strategies for storage assisted renewable power producer in hour ahead market"
    - "online convex optimization using predictions"
    - "a tale of two metrics simultaneous bounds on competitiveness and regret"
    - "peak aware online economic dispatching for microgrids"
    - "the submodular secretary problem goes linear"
    - "online energy generation scheduling for microgrids with intermittent energy sources and co generation"

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.PF(Performance)