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: "adaptive and resilient revenue maximizing dynamic resource allocation and pricing for cloud enabled iot systems"
  Abstract: Cloud computing is becoming an essential component of modern computer and communication systems. The available resources at the cloud such as computing nodes, storage, databases, etc. are often packaged in the form of virtual machines (VMs) to be used by remotely located client applications for computational tasks. However, the cloud has a limited number of VMs available, which have to be efficiently utilized to generate higher productivity and subsequently generate maximum revenue. Client applications generate requests with computational tasks at random times with random complexity to be processed by the cloud. The cloud service provider (CSP) has to decide whether to allocate a VM to a task at hand or to wait for a higher complexity task in the future. We propose a threshold-based mechanism to optimally decide the allocation and pricing of VMs to sequentially arriving requests in order to maximize the revenue of the CSP over a finite time horizon. Moreover, we develop an adaptive and resilient framework based that can counter the effect of realtime changes in the number of available VMs at the cloud server, the frequency and nature of arriving tasks on the revenue of the CSP.

Connected through hyperedge HE-1 (induced by source paper "joint dynamic pricing and radio resource allocation framework for iot services"):
  with members:
    - "computing resource allocation in three tier iot fog networks a joint optimization approach combining stackelberg game and matching"
    - "toward hierarchical mobile edge computing an auction based profit maximization approach"
    - "market model and optimal pricing scheme of big data and internet of things iot"
    - "mobile data transactions in device to device communication networks pricing and auction"
    - "multiobjective signal processing optimization the way to balance conflicting metrics in 5g systems"
    - "price based resource allocation for spectrum sharing femtocell networks a stackelberg game approach"

Connected through hyperedge HE-2 (induced by source paper "optimal two sided market mechanism design for large scale data sharing and trading in massive iot networks"):
  with members:
    - "sensing as a service s2aas buying and selling iot data"
    - "the emerging internet of things marketplace from an industrial perspective a survey"
    - "sensing as a service model for smart cities supported by internet of things"

Connected through hyperedge HE-3 (induced by source paper "dynamic spatio temporal resource provisioning for on demand urban services in smart cities"):
  with members:
    - "green networking in cellular hetnets a unified radio resource management framework with base station on off switching"
    - "data driven robust taxi dispatch under demand uncertainties"

Connected through hyperedge HE-4 (induced by source paper "optimal dynamic contract for spectrum reservation in mission critical unb iot systems"):
  with members:
    - "an enhanced access reservation protocol with a partial preamble transmission mechanism in nb iot systems"
    - "optimizing mission critical data dissemination in massive iot networks"
    - "2d time frequency interference modelling using stochastic geometry for performance evaluation in low power wide area networks"
    - "spectrum reservation contract design in tv white space networks"

Connected through hyperedge HE-5 (induced by source paper "real time transmission mechanism design for wireless iot sensors with energy harvesting under power saving mode"):
  with members:
    - "low power wide area networks an overview"
    - "energy harvesting networks with energy cooperation procrastinating policies"

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)