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: "computing resource allocation in three tier iot fog networks a joint optimization approach combining stackelberg game and matching"
  Abstract: Fog computing is a promising architecture to provide economic and low latency data services for future Internet of things (IoT)-based network systems. It relies on a set of low-power fog nodes that are close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of fog nodes to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of fog nodes (FNs) to all the DSSs to achieve an optimal and stable performance is an important problem. In this paper, we propose a joint optimization framework for all FNs, DSOs and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems.

Connected through hyperedge HE-1 (induced by source paper "distributed resource allocation for network slicing of bandwidth and computational resource"):
  with members:
    - "distributed resource allocation for network slicing over licensed and unlicensed bands"
    - "joint optimization of computation and communication power in multi user massive mimo systems"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "heterogeneous cellular networks with spatio temporal traffic delay analysis and scheduling"
    - "spatial spectrum and energy efficiency of random cellular networks"
    - "5g ultra dense cellular networks"

Connected through hyperedge HE-2 (induced by source paper "let s share a game theoretic framework for resource sharing in mobile edge clouds"):
  with members:
    - "a game theoretic framework for resource sharing in clouds"
    - "a game theoretic approach to multi objective resource sharing and allocation in mobile edge"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"

Connected through hyperedge HE-3 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "machine learning in iot security current solutions and future challenges"
    - "toward intelligent vehicular networks a machine learning framework"
    - "distributed learning for channel allocation over a shared spectrum"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-4 (induced by source paper "joint dynamic pricing and radio resource allocation framework for iot services"):
  with members:
    - "adaptive and resilient revenue maximizing dynamic resource allocation and pricing for cloud enabled iot systems"
    - "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-5 (induced by source paper "multi operator network sharing for massive iot"):
  with members:
    - "distributed resource allocation for network slicing over licensed and unlicensed bands"
    - "massive non orthogonal multiple access for cellular iot potentials and limitations"
    - "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"
    - "from network sharing to multi tenancy the 5g network slice broker"

Connected through hyperedge HE-6 (induced by source paper "how to place your apps in the fog state of the art and open challenges"):
  with members:
    - "a survey of energy efficiency in sdn software based methods and optimization models"
    - "fogstore toward a distributed data store for fog computing"
    - "online placement of multi component applications in edge computing environments"

Connected through hyperedge HE-7 (induced by source paper "dynamic task offloading and resource allocation for ultra reliable low latency edge computing"):
  with members:
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "proactive edge computing in latency constrained fog networks"
    - "millimeter wave v2v communications distributed association and beam alignment"
    - "a survey on mobile edge computing the communication perspective"
    - "an online secretary framework for fog network formation with minimal latency"

Connected through hyperedge HE-8 (induced by source paper "dynamic network slicing for scalable fog computing systems with energy harvesting"):
  with members:
    - "distributed optimization for energy efficient fog computing in the tactile internet"
    - "sdfog a software defined computing architecture for qos aware service orchestration over edge devices"
    - "response time optimized distributed cloud resource allocation"
    - "fog computing principles architectures and applications"
    - "from network sharing to multi tenancy the 5g network slice broker"
    - "network slicing games enabling customization in multi tenant networks"
    - "spatial spectrum and energy efficiency of random cellular networks"
    - "energy harvesting wireless communications a review of recent advances"

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.GT(Computer Science and Game Theory)