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: "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
  Abstract: In a software-defined radio access network (RAN), a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a software-defined RAN, where a limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from competition with other SPs for the orchestration of channel access opportunities over its MUs, which request both mobile-edge computing and traditional cellular services in the slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the network dynamics as well as the control policies of its competitors. We propose an abstract stochastic game to approximate the Nash equilibrium. The selfish behaviours of a SP can then be characterized by a single-agent Markov decision process (MDP). To simplify decision makings, we linearly decompose the per-SP MDP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.

Connected through hyperedge HE-1 (induced by source paper "learning to branch accelerating resource allocation in wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "energy efficient joint resource allocation and power control for d2d communications"
    - "radio resource allocation for device to device underlay communication using hypergraph theory"
    - "a survey on device to device communication in cellular networks"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-2 (induced by source paper "deep learning based wireless resource allocation with application to vehicular networks"):
  with members:
    - "learning to branch accelerating resource allocation in wireless networks"
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "learning to detect"
    - "learning optimal resource allocations in wireless systems"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "distributed power control for large energy harvesting networks a multi agent deep reinforcement learning approach"
    - "power allocation in multi user cellular networks deep reinforcement learning approaches"
    - "toward intelligent vehicular networks a machine learning framework"

Connected through hyperedge HE-3 (induced by source paper "age of information aware radio resource management in vehicular networks a proactive deep reinforcement learning perspective"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "minimizing the age of information in wireless networks with stochastic arrivals"
    - "learning to entangle radio resources in vehicular communications an oblivious game theoretic perspective"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "deep learning with long short term memory for time series prediction"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "performance optimization in mobile edge computing via deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "management and orchestration of virtual network functions via deep reinforcement learning"):
  with members:
    - "deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing"
    - "machine learning in the air"
    - "deep reinforcement learning for resource management in network slicing"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "auto scaling network resources using machine learning to improve qos and reduce cost"
    - "addressing function approximation error in actor critic methods"
    - "deep learning in mobile and wireless networking a survey"
    - "comparative study of virtual machines and containers for devops developers"

Connected through hyperedge HE-5 (induced by source paper "resource awareness in unmanned aerial vehicle assisted mobile edge computing systems"):
  with members:
    - "age of information aware radio resource management in vehicular networks a proactive deep reinforcement learning perspective"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "multiuser computation offloading and downloading for edge computing with virtualization"
    - "a survey on mobile edge computing the communication perspective"
    - "latency and reliability aware task offloading and resource allocation for mobile edge computing"
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"
    - "deep recurrent q learning for partially observable mdps"

Connected through hyperedge HE-6 (induced by source paper "application of machine learning in wireless networks key techniques and open issues"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for resource management in network slicing"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "deep learning for radio resource allocation in multi cell networks"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "deep learning in mobile and wireless networking a survey"
    - "handover control in wireless systems via asynchronous multi user deep reinforcement learning"

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)