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: "low complexity distributed radio access network slicing algorithms and experimental results"
  Abstract: Radio access network (RAN) slicing is an effective methodology to dynamically allocate networking resources in 5G networks. One of the main challenges of RAN slicing is that it is provably an NP-Hard problem. For this reason, we design near-optimal low-complexity distributed RAN slicing algorithms. First, we model the slicing problem as a congestion game, and demonstrate that such game admits a unique Nash equilibrium (NE). Then, we evaluate the Price of Anarchy (PoA) of the NE, i.e., the efficiency of the NE as compared to the social optimum, and demonstrate that the PoA is upper-bounded by 3/2. Next, we propose two fully-distributed algorithms that provably converge to the unique NE without revealing privacy-sensitive parameters from the slice tenants. Moreover, we introduce an adaptive pricing mechanism of the wireless resources to improve the network owner's profit. We evaluate the performance of our algorithms through simulations and an experimental testbed deployed on the Amazon EC2 cloud, both based on a real-world dataset of base stations from the OpenCellID project. Results conclude that our algorithms converge to the NE rapidly and achieve near-optimal performance, while our pricing mechanism effectively improves the profit of the network owner.

Connected through hyperedge HE-1 (induced by source paper "constrained network slicing games achieving service guarantees and network efficiency"):
  with members:
    - "statistical multiplexing and traffic shaping games for network slicing"
    - "network slicing games enabling customization in multi tenant networks"
    - "user association for load balancing in heterogeneous cellular networks"

Connected through hyperedge HE-2 (induced by source paper "gan based deep distributional reinforcement learning for resource management in network slicing"):
  with members:
    - "deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "slice as an evolutionary service genetic optimization for inter slice resource management in 5g networks"
    - "implicit quantile networks for distributional reinforcement learning"
    - "distributed distributional deterministic policy gradients"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "a distributional perspective on reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "optimal and fast real time resources slicing with deep dueling neural networks"):
  with members:
    - "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"
    - "statistical multiplexing and traffic shaping games for network slicing"
    - "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 "toward operator to waveform 5g radio access network slicing"):
  with members:
    - "big data goes small real time spectrum driven embedded wireless networking through deep learning in the rf loop"
    - "securing the internet of things in the age of machine learning and software defined networking"
    - "wnos an optimization based wireless network operating system"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"
    - "a survey on low latency towards 5g ran core network and caching solutions"
    - "survey on network virtualization hypervisors for software defined networking"
    - "from network sharing to multi tenancy the 5g network slice broker"

Connected through hyperedge HE-5 (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"
    - "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"
    - "network slicing games enabling customization in multi tenant networks"

Connected through hyperedge HE-6 (induced by source paper "a coverage aware resource provisioning method for network slicing"):
  with members:
    - "impact of processing resource sharing on the placement of chained virtual network functions"
    - "network service orchestration a survey"
    - "on the trade off between computational load and reliability for network function virtualization"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"
    - "understanding the computational requirements of virtualized baseband units using a programmable cloud radio access network testbed"
    - "propagation path loss models for 5g urban micro and macro cellular scenarios"

Connected through hyperedge HE-7 (induced by source paper "the slice is served enforcing radio access network slicing in virtualized 5g systems"):
  with members:
    - "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"
    - "network slicing games enabling customization in multi tenant networks"
    - "evolution toward 5g multi tier cellular wireless networks an interference management perspective"

Connected through hyperedge HE-8 (induced by source paper "multi tenant cross slice resource orchestration a deep reinforcement learning approach"):
  with members:
    - "distributed resource allocation for network slicing over licensed and unlicensed bands"
    - "dynamic network slicing for scalable fog computing systems with energy harvesting"
    - "performance optimization in mobile edge computing via deep reinforcement learning"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "a survey on mobile edge computing the communication perspective"
    - "near optimal behavior via approximate state abstraction"
    - "dynamic computation offloading for mobile edge computing with energy harvesting devices"
    - "from network sharing to multi tenancy the 5g network slice broker"

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)