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: "network slicing games enabling customization in multi tenant networks"
  Abstract: Network slicing to enable resource sharing among multiple tenants --network operators and/or services-- is considered a key functionality for next generation mobile networks. This paper provides an analysis of a well-known model for resource sharing, the 'share-constrained proportional allocation' mechanism, to realize network slicing. This mechanism enables tenants to reap the performance benefits of sharing, while retaining the ability to customize their own users' allocation. This results in a network slicing game in which each tenant reacts to the user allocations of the other tenants so as to maximize its own utility. We show that, under appropriate conditions, the game associated with such strategic behavior converges to a Nash equilibrium. At the Nash equilibrium, a tenant always achieves the same, or better, performance than under a static partitioning of resources, hence providing the same level of protection as such static partitioning. We further analyze the efficiency and fairness of the resulting allocations, providing tight bounds for the price of anarchy and envy-freeness. Our analysis and extensive simulation results confirm that the mechanism provides a comprehensive practical solution to realize network slicing. Our theoretical results also fill a gap in the literature regarding the analysis of this resource allocation model under strategic players.

Connected through hyperedge HE-1 (induced by source paper "machine learning for network slicing resource management a comprehensive survey"):
  with members:
    - "a utility driven multi queue admission control solution for network slicing"
    - "a markov model of slice admission control"
    - "slice as an evolutionary service genetic optimization for inter slice resource management in 5g networks"
    - "rational impatience admission control in 5g sliced networks shall i bide my slice opportunity"
    - "statistical multiplexing and traffic shaping games for network slicing"
    - "a profit maximizing strategy of network resource management for 5g tenant slices"

Connected through hyperedge HE-2 (induced by source paper "constrained network slicing games achieving service guarantees and network efficiency"):
  with members:
    - "low complexity distributed radio access network slicing algorithms and experimental results"
    - "statistical multiplexing and traffic shaping games for network slicing"
    - "user association for load balancing in heterogeneous cellular networks"

Connected through hyperedge HE-3 (induced by source paper "how should i orchestrate resources of my slices for bursty urllc service provision"):
  with members:
    - "embb urllc resource slicing a risk sensitive approach"
    - "low complexity distributed radio access network slicing algorithms and experimental results"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "non orthogonal embb urllc radio access for cloud radio access networks with analog fronthauling"
    - "statistical multiplexing and traffic shaping games for network slicing"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"

Connected through hyperedge HE-4 (induced by source paper "the slice is served enforcing radio access network slicing in virtualized 5g systems"):
  with members:
    - "low complexity distributed radio access network slicing algorithms and experimental results"
    - "slice as an evolutionary service genetic optimization for inter slice resource management in 5g networks"
    - "a learning approach for low complexity optimization of energy efficiency in multicarrier wireless networks"
    - "evolution toward 5g multi tier cellular wireless networks an interference management perspective"

Connected through hyperedge HE-5 (induced by source paper "a markov model of slice admission control"):
  with members:
    - "slice as an evolutionary service genetic optimization for inter slice resource management in 5g networks"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"
    - "modeling profit of sliced 5g networks for advanced network resource management and slice implementation"

Connected through hyperedge HE-6 (induced by source paper "learning assisted secure end to end network slicing for cyber physical systems"):
  with members:
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"

Connected through hyperedge HE-7 (induced by source paper "competition between tenants with strategic resource allocation in 5g network slicing"):
  with members:
    - "from network sharing to multi tenancy the 5g network slice broker"

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"
    - "computing resource allocation in three tier iot fog networks a joint optimization approach combining stackelberg game and matching"
    - "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"
    - "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)