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: "on the interaction between personal comfort systems and centralized hvac systems in office buildings"
  Abstract: Most modern HVAC systems suffer from two intrinsic problems. First, inability to meet diverse comfort requirements of the occupants. Second, heat or cool an entire zone even when the zone is only partially occupied. Both issues can be mitigated by using personal comfort systems (PCS) which bridge the comfort gap between what is provided by a central HVAC system and the personal preferences of the occupants. In recent work, we have proposed and deployed such a system, called SPOT. #R##N#We address the question, "How should an existing HVAC system modify its operation to benefit the availability of PCS like SPOT?" For example, energy consumption could be reduced during sparse occupancy by choosing appropriate thermal set backs, with the PCS providing the additional offset in thermal comfort required for each occupant. Our control strategy based on Model Predictive Control (MPC), employs a bi-linear thermal model, and has two time-scales to accommodate the physical constraints that limit certain components of the central HVAC system from frequently changing their set points. #R##N#We compare the energy consumption and comfort offered by our SPOT-aware HVAC system with that of a state-of-the-art MPC-based central HVAC system in multiple settings including different room layouts and partial deployment of PCS. Numerical evaluations show that our system obtains, in average, 45% (15%) savings in energy in summer (winter), compared with the benchmark system for the case of homogeneous comfort requirements. For heterogeneous comfort requirements, we observe 51% (29%) improvement in comfort in summer (winter) in addition to significant savings in energy.

Connected through hyperedge HE-1 (induced by source paper "beyond control enabling smart thermostats for leakage detection"):
  with members:
    - "using personal environmental comfort systems to mitigate the impact of occupancy prediction errors on hvac performance"
    - "an in depth study into using emi signatures for appliance identification"

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)