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: "slice as an evolutionary service genetic optimization for inter slice resource management in 5g networks"
  Abstract: In the context of fifth-generation mobile networks, the concept of “Slice as a Service” promotes mobile network operators to flexibly share infrastructures with mobile service providers and stakeholders. However, it also challenges with an emerging demand for efficient online algorithms to optimize the request-and-decision-based inter-slice resource management strategy. Based on genetic algorithms, this paper presents a novel online optimizer that efficiently approaches toward the ideal slicing strategy with maximized long-term network utility. The proposed method encodes slicing strategies into binary sequences to cope with the request-and-decision mechanism. It requires no  a priori  knowledge about the traffic/utility models and therefore supports heterogeneous slices while providing solid effectiveness, good robustness against non-stationary service scenarios, and high scalability.

Connected through hyperedge HE-1 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges"):
  with members:
    - "a novel qoe aware sdn enabled nfv based management architecture for future multimedia applications on 5g systems"
    - "securing fog to things environment using intrusion detection system based on ensemble learning"
    - "wireless network slicing generalized kelly mechanism based resource allocation"
    - "resource orchestration of 5g transport networks for vertical industries"
    - "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 survey on mobile edge computing the communication perspective"
    - "nfv and sdn key technology enablers for 5g networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "network slicing games enabling customization in multi tenant networks"

Connected through hyperedge HE-3 (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"
    - "low complexity distributed radio access network slicing algorithms and experimental results"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "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-4 (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"
    - "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-5 (induced by source paper "deep reinforcement learning for network slicing with heterogeneous resource requirements and time varying traffic dynamics"):
  with members:
    - "network slicing to enable scalability and flexibility in 5g mobile networks"
    - "network function virtualization state of the art and research challenges"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "joint resource and admission management for slice enabled networks"):
  with members:
    - "optimal slice allocation in 5g core networks"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "from network sharing to multi tenancy the 5g network slice broker"

Connected through hyperedge HE-7 (induced by source paper "admission control and resource allocation of slice requests with minimization of mobile service provider s cost"):
  with members:
    - "optimal slice allocation in 5g core networks"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "from network sharing to multi tenancy the 5g network slice broker"

Connected through hyperedge HE-8 (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"
    - "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"

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.NE(Neural and Evolutionary Computing)