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: "harnessing constrained resources in service industry via video analytics"
  Abstract: Service industries contribute significantly to many developed and developing - economies. As their business activities expand rapidly, many service companies struggle to maintain customer's satisfaction due to sluggish service response caused by resource shortages. Anticipating resource shortages and proffering solutions before they happen is an effective way of reducing the adverse effect on operations. However, this proactive approach is very expensive in terms of capacity and labor costs. Many companies fall into productivity conundrum as they fail to find sufficient strong arguments to justify the cost of a new technology yet cannot afford not to invest in new technologies to match up with competitors. The question is whether there is an innovative solution to maximally utilize available resources and drastically reduce the effect that the shortages of resources may cause yet achieving high level of service quality at a low cost. This work demonstrates with a practical analysis of a trolley tracking system we designed and deployed at Hong Kong International Airport (HKIA) on how video analytics helps achieve management's goal of satisfying customer's needs via real-time detection and prevention of problems they may encounter during the service consumption process using existing video technology rather than adopting new technologies. This paper presents the integration of commercial video surveillance system with deep learning algorithms for video analytics. We show that our system can provide accurate decision when faced with total or partial occlusion with high accuracy and it significantly improves daily operation. It is envisioned that this work will heighten the appreciation of integrative technologies for resource management within the service industries and as a measure for real-time customer assistance.

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.CY(Computers and Society)