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: "statistical multiplexing and traffic shaping games for network slicing"
  Abstract: Next generation wireless architectures are expected to enable slices of shared wireless infrastructure which are customized to specific mobile operators/services. Given infrastructure costs and stochastic nature of mobile services' spatial loads, it is highly desirable to achieve efficient statistical multiplexing amongst network slices. We study a simple dynamic resource sharing policy which allocates a 'share' of a pool of (distributed) resources to each slice- Share Constrained Proportionally Fair (SCPF). We give a characterization of the achievable performance gains over static slicing, showing higher gains when a slice's spatial load is more 'imbalanced' than, and/or 'orthogonal' to, the aggregate network load. Under SCPF, traditional network dimensioning translates to a coupled share dimensioning problem, addressing the existence of a feasible share allocation given slices' expected loads and performance requirements. We provide a solution to robust share dimensioning for SCPF-based network slicing. Slices may wish to unilaterally manage their users' performance via admission control which maximizes their carried loads subject to performance requirements. We show this can be modeled as a "traffic shaping" game with an achievable Nash equilibrium. Under high loads, the equilibrium is explicitly characterized, as are the gains in the carried load under SCPF vs. static slicing. Detailed simulations of a wireless infrastructure supporting multiple slices with heterogeneous mobile loads show the fidelity of our models and range of validity of our high load equilibrium analysis.

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"
    - "a profit maximizing strategy of network resource management for 5g tenant slices"
    - "network slicing games enabling customization in multi tenant networks"

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"
    - "network slicing games enabling customization in multi tenant networks"
    - "user association for load balancing in heterogeneous cellular networks"

Connected through hyperedge HE-3 (induced by source paper "optimal and fast real time resources slicing with deep dueling neural networks"):
  with members:
    - "low complexity distributed radio access network slicing algorithms and experimental results"
    - "reinforcement learning based qos qoe aware service function chaining in software driven 5g slices"
    - "slice as an evolutionary service genetic optimization for inter slice resource management in 5g networks"
    - "optimal slice allocation in 5g core networks"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"
    - "network slicing games enabling customization in multi tenant 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.NI(Networking and Internet Architecture)