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: "optimal caching designs for perfect imperfect and unknown file popularity distributions in large scale multi tier wireless networks"
  Abstract: Most existing caching solutions for wireless networks rest on an unrealistic assumption that the file popularity distribution is perfectly known. In this paper, we consider optimal caching designs for perfect, imperfect and unknown file popularity distributions in large-scale multi-tier wireless networks. First, in the case of perfect file popularity distribution, we formulate the optimization problem to maximize the successful transmission probability (STP), which is a nonconvex problem. We develop an efficient parallel iterative algorithm to obtain a stationary point using parallel successive convex approximation (SCA). Then, in the case of imperfect file popularity distribution, we formulate the robust optimization problem to maximize the worst-case STP. To solve this challenging maximin problem, we transform it to an equivalent complementary geometric programming (CGP), and develop an efficient iterative algorithm which is shown to converge to a stationary point using SCA. To the best of our knowledge, this is the first work explicitly considering the estimation error of file popularity distribution in the optimization of caching design. Next, in the case of unknown file popularity distribution, we formulate the stochastic optimization problem to maximize the stochastic STP (i.e., the STP in the stochastic form), which is a challenging nonconvex stochastic optimization problem. Based on stochastic parallel SCA, we develop an efficient iterative algorithm to obtain a stationary point, by exploiting structural properties of the stochastic STP and making full use of instantaneous file requests. As far as we know, this is the first work considering stochastic optimization in a large-scale multi-tier wireless network. Finally, by numerical results, we show that the proposed solutions achieve significant gains over existing schemes in all three cases.

Connected through hyperedge HE-1 (induced by source paper "joint optimization of file placement and delivery in cache assisted wireless networks with limited lifetime and cache space"):
  with members:
    - "joint downlink scheduling for file placement and delivery in cache assisted wireless networks with finite file lifetime"
    - "online user scheduling and resource allocation for mobile edge computing systems"
    - "cooperative job dispatching in edge computing network with unpredictable uploading delay"
    - "mdp based scheduling design for mobile edge computing systems with random user arrival"
    - "modeling analysis and optimization of coded caching in small cell networks"
    - "stochastic content centric multicast scheduling for cache enabled heterogeneous cellular networks"
    - "wireless device to device caching networks basic principles and system performance"
    - "optimal caching and user association in cache enabled heterogeneous wireless networks"

Connected through hyperedge HE-2 (induced by source paper "analysis and optimization of caching and multicasting for multi quality videos in large scale wireless networks"):
  with members:
    - "wireless video caching and dynamic streaming under differentiated quality requirements"
    - "analysis and optimization of caching and multicasting in large scale cache enabled heterogeneous wireless networks"
    - "optimal geographic caching in cellular networks"
    - "analysis and optimization of caching and multicasting in large scale cache enabled wireless networks"
    - "joint resource partitioning and offloading in heterogeneous cellular networks"
    - "downlink capacity and base station density in cellular networks"

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.IT(Information Theory)