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: "optimal slice allocation in 5g core networks"
  Abstract: Network slicing is the key to providing flexible, scalable and on-demand solutions for the vast array of applications in 5G networks. Two key challenges of 5G networks are the network slicing and guaranteeing end-to-end delay for a slice. In this paper, we address the question of optimal allocation of a slice in 5G core networks. We adopt and extend the work by D. Dietrich et al. [1] to create a model that satisfies constraints on end-to-end delay as well as isolation between components of a slice for reliability. Our work takes into consideration Service Level Agreement and performance parameters. Our results show the impact of varying parameters on the utilization of the system.

Connected through hyperedge HE-1 (induced by source paper "proactive allocation as defense for malicious co residency in sliced 5g core networks"):
  with members:
    - "towards secure slicing using slice isolation to mitigate ddos attacks on 5g core network slices"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"
    - "an end to end network slicing framework for 5g wireless communication systems"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "joint resource and admission management for slice enabled networks"):
  with members:
    - "slice as an evolutionary service genetic optimization for inter slice resource management in 5g 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-4 (induced by source paper "admission control and resource allocation of slice requests with minimization of mobile service provider s cost"):
  with members:
    - "slice as an evolutionary service genetic optimization for inter slice resource management in 5g 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-5 (induced by source paper "towards secure slicing using slice isolation to mitigate ddos attacks on 5g core network slices"):
  with members:
    - "end to end simulation of 5g mmwave networks"
    - "onets online network slice broker from theory to practice"
    - "provisioning low latency resilient mobile edge clouds for 5g"

Connected through hyperedge HE-6 (induced by source paper "dsaf dynamic slice allocation framework for 5g core network"):
  with members:
    - "towards secure slicing using slice isolation to mitigate ddos attacks on 5g core network slices"
    - "provisioning low latency resilient mobile edge clouds for 5g"

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)