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: "edgemore improving resource allocation with multiple options from tenants"
  Abstract: Under the paradigm of Edge Computing (EC), a Network Operator (NO) deploys computational resources at the network edge and let third-party services run on top of them. Besides the clear advantages for Service Providers (SPs) and final users thanks to the vicinity of computation nodes, a NO aims to allocate edge resources in order to increase its own utility, including bandwidth saving, operational cost reduction, QoE for its user, etc. However, while the number of third-party services competing for edge resources is expected to dramatically grow, the resources deployed cannot increase accordingly due to physical limitations. Therefore, other strategies are needed to fully exploit the potential of EC, despite its constrains. #R##N#To this aim we propose to leverage \emph{service adaptability}, a dimension that has mainly been neglected so far. Each service can adapt to the amount of resources that the NO has allocated to it, balancing the fraction of service computation performed locally at the edge and relying on remote servers, e.g., in the Cloud, for the rest. We propose \emph{EdgeMORE}, a resource allocation strategy in which SPs declare the different configurations to which they are able to adapt, specifying the resources needed and the expected utility. The NO then chooses the most convenient option per each SP, in order to maximize the total utility. #R##N#We formalize EdgeMORE as a Integer Linear Programming and we propose a polynomial time heuristic. We show via simulation that, by letting SPs declare their ability to adapt to different constrained configurations and letting the NO choose between them, EdgeMORE greatly improves EC utility and resource utilization.

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.DC(Distributed, Parallel, and Cluster Computing)