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: "a survey on applications of model free strategy learning in cognitive wireless networks"
  Abstract: The framework of cognitive wireless networks is expected to endow the wireless devices with the cognition-intelligence ability with which they can efficiently learn and respond to the dynamic wireless environment. In many practical scenarios, the complexity of network dynamics makes it difficult to determine the network evolution model in advance. Thus, the wireless decision-making entities may face a black-box network control problem and the model-based network management mechanisms will be no longer applicable. In contrast, model-free learning enables the decision-making entities to adapt their behaviors based on the reinforcement from their interaction with the environment and (implicitly) build their understanding of the system from scratch through trial-and-error. Such characteristics are highly in accordance with the requirement of cognition-based intelligence for devices in cognitive wireless networks. Therefore, model-free learning has been considered as one key implementation approach to adaptive, self-organized network control in cognitive wireless networks. In this paper, we provide a comprehensive survey on the applications of the state-of-the-art model-free learning mechanisms in cognitive wireless networks. According to the system models on which those applications are based, a systematic overview of the learning algorithms in the domains of single-agent system, multiagent systems, and multiplayer games is provided. The applications of model-free learning to various problems in cognitive wireless networks are discussed with the focus on how the learning mechanisms help to provide the solutions to these problems and improve the network performance over the model-based, non-adaptive methods. Finally, a broad spectrum of challenges and open issues is discussed to offer a guideline for the future research directions.

Connected through hyperedge HE-1 (induced by source paper "spectrum intelligent radio technology development and future trends"):
  with members:
    - "mobile collaborative spectrum sensing for heterogeneous networks a bayesian machine learning approach"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "learning based constraint satisfaction with sensing restrictions"

Connected through hyperedge HE-2 (induced by source paper "game theory for multi access edge computing survey use cases and future trends"):
  with members:
    - "non cooperative game theory based rate adaptation for dynamic video streaming over http"
    - "fog computing a taxonomy survey and future directions"
    - "optimal pricing for a peer to peer sharing platform under network externalities"
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "a survey on mobile edge computing the communication perspective"
    - "sustainable incentives for mobile crowdsensing"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "mobile edge computing a survey on architecture and computation offloading"

Connected through hyperedge HE-3 (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"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "deep learning in mobile and wireless networking a survey"

Connected through hyperedge HE-4 (induced by source paper "machine learning for heterogeneous ultra dense networks with graphical representations"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "learning to decode linear codes using deep learning"
    - "scalable uplink signal detection in c rans via randomized gaussian message passing"
    - "large scale distributed semi supervised learning using streaming approximation"

Connected through hyperedge HE-5 (induced by source paper "survey of game theory and future trends with application to emerging wireless data communication networks"):
  with members:
    - "non cooperative game theory based rate adaptation for dynamic video streaming over http"
    - "fog computing a taxonomy survey and future directions"
    - "a survey on mobile edge computing the communication perspective"
    - "sustainable incentives for mobile crowdsensing"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "game theory models for communication between agents a review"
    - "game theoretic multi agent control and network cost allocation under communication constraints"
    - "co primary spectrum sharing for inter operator device to device communication"

Connected through hyperedge HE-6 (induced by source paper "performance analysis of trial and error algorithms"):
  with members:
    - "distributed power allocation with sinr constraints using trial and error 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)