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: "joint user association and resource allocation in virtualized wireless networks"
  Abstract: In this paper, we consider a down-link transmission of multicell virtualized wireless networks (VWNs) where users of different service providers (slices) within a specific region are served by a set of base stations (BSs) through orthogonal frequency division multiple access (OFDMA). In particular, we develop a joint BS assignment, sub-carrier and power allocation algorithm to maximize the network throughput, while satisfying the minimum required rate of each slice. Under the assumption that each user at each transmission instance can connect to no more than one BS, we introduce the user-association factor (UAF) to represent the joint sub-carrier and BS assignment as the optimization variable vector in the mathematical problem formulation. Sub-carrier reuse is allowed in different cells, but not within one cell. As the proposed optimization problem is inherently non-convex and NP-hard, by applying the successive convex approximation (SCA) and complementary geometric programming (CGP), we develop an efficient two-step iterative approach with low computational complexity to solve the proposed problem. For a given power-allocation, Step 1 derives the optimum userassociation and subsequently, for an obtained user-association, Step 2 find the optimum power-allocation. Simulation results demonstrate that the proposed iterative algorithm outperforms the traditional approach in which each user is assigned to the BS with the largest average value of signal strength, and then, joint sub-carrier and power allocation is obtained for the assigned users of each cell. Especially, for the cell-edge users, simulation results reveal a coverage improvement up to 57% and 71% for uniform and non-uniform users distribution, respectively leading to more reliable transmission and higher spectrum efficiency for VWN.

Connected through hyperedge HE-1 (induced by source paper "mode selection and resource allocation in sliced fog radio access networks a reinforcement learning approach"):
  with members:
    - "application of machine learning in wireless networks key techniques and open issues"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "fog computing based radio access networks issues and challenges"

Connected through hyperedge HE-2 (induced by source paper "cloud based queuing model for tactile internet in next generation of ran"):
  with members:
    - "resource management and admission control for tactile internet in next generation of ran"
    - "joint uplink and downlink resource configuration for ultra reliable and low latency communications"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "realizing the tactile internet haptic communications over next generation 5g cellular networks"
    - "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"
    - "uplink transmission design with massive machine type devices in tactile internet"
    - "fronthauling for 5g lte u ultra dense cloud small cell networks"

Connected through hyperedge HE-3 (induced by source paper "resource management and admission control for tactile internet in next generation of ran"):
  with members:
    - "improving network availability of ultra reliable and low latency communications with multi connectivity"
    - "joint uplink and downlink resource configuration for ultra reliable and low latency communications"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "realizing the tactile internet haptic communications over next generation 5g cellular networks"
    - "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"
    - "uplink transmission design with massive machine type devices in tactile internet"
    - "fronthauling for 5g lte u ultra dense cloud small cell networks"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"

Connected through hyperedge HE-4 (induced by source paper "stochastic optimization and control framework for 5g network slicing with effective isolation"):
  with members:
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "defining and surveying wireless link virtualization and wireless network virtualization"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"

Connected through hyperedge HE-5 (induced by source paper "leveraging synergy of 5g sdwn and multi layer resource management for network optimization"):
  with members:
    - "virtualization of multi cell 802 11 networks association and airtime control"
    - "performance analysis of massive mimo for cell boundary users"
    - "5g cellular key enabling technologies and research challenges"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"
    - "optimal user association for massive mimo empowered ultra dense wireless networks"
    - "optimal user cell association for massive mimo wireless 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)