human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "online combinatorial optimization for interconnected refrigeration systems linear approximation and submodularity"):
  Abstract: Commercial refrigeration systems consume 7% of the total commercial energy consumption in the United States. Improving their energy efficiency contributes to the sustainability of global energy systems and the supermarket business sector. This paper proposes a new control method that can save the energy consumption of multi-case supermarket refrigerators by explicitly taking into account their interconnected and switched system dynamics. Its novelty is a bilevel combinatorial optimization formulation to generate ON/OFF control actions for expansion valves and compressors. The inner optimization module keeps display case temperatures in a desirable range and the outer optimization module minimizes energy consumption. In addition to its energy-saving capability, the proposed controller significantly reduces the frequency of compressor switchings by employing a conservative compressor control strategy. However, solving this bilevel optimization problem associated with interconnected and switched systems is a computationally challenging task. To solve the problem in near real time, we propose two approximation algorithms that can solve both the inner and outer optimization problems at once. The first algorithm uses a linear approximation, and the second is based on the submodular structure of the optimization problem. Both are (polynomial-time) scalable algorithms and generate near-optimal solutions with performance guarantees. Our work complements existing optimization-based control methods (e.g., MPC) for supermarket refrigerators, as our algorithms can be adopted as a tool for solving combinatorial optimization problems arising in these methods.

Includes members:
  Member "maximizing sequence submodular functions and its application to online advertising":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive sequence submodularity")
  Member "adaptive submodularity theory and applications in active learning and stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "comprehensive and efficient data labeling via adaptive model scheduling")

Question: Please tell me which class the source paper 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)