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: "deep multi user reinforcement learning for distributed dynamic spectrum access"
  Abstract: We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into K orthogonal channels. In the beginning of each time slot, each user selects a channel and transmits a packet with a certain transmission probability. After each time slot, each user that has transmitted a packet receives a local observation indicating whether its packet was successfully delivered or not (i.e., ACK signal). The objective is a multi-user strategy for accessing the spectrum that maximizes a certain network utility in a distributed manner without online coordination or message exchanges between users. Obtaining an optimal solution for the spectrum access problem is computationally expensive in general due to the large state space and partial observability of the states. To tackle this problem, we develop a novel distributed dynamic spectrum access algorithm based on deep multi-user reinforcement leaning. Specifically, at each time slot, each user maps its current state to spectrum access actions based on a trained deep-Q network used to maximize the objective function. Game theoretic analysis of the system dynamics is developed for establishing design principles for the implementation of the algorithm. Experimental results demonstrate strong performance of the algorithm.

Connected through hyperedge HE-1 (induced by source paper "pops policy pruning and shrinking for deep reinforcement learning"):
  with members:
    - "legr filter pruning via learned global ranking"
    - "structured pruning of neural networks with budget aware regularization"
    - "amc automl for model compression and acceleration on mobile devices"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "layer compensated pruning for resource constrained convolutional neural networks"
    - "deep learning in mobile and wireless networking a survey"
    - "deep reinforcement one shot learning for artificially intelligent classification systems"
    - "deep reinforcement learning an overview"

Connected through hyperedge HE-2 (induced by source paper "mac protocol design optimization using deep learning"):
  with members:
    - "unboxing mac protocol design optimization using deep learning"
    - "word2ket space efficient word embeddings inspired by quantum entanglement"
    - "internet congestion control via deep reinforcement learning"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"
    - "proactive resource management in lte u systems a deep learning perspective"

Connected through hyperedge HE-3 (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-4 (induced by source paper "thirty years of machine learning the road to pareto optimal next generation wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "an introduction to deep learning for the physical layer"
    - "budget constrained multi armed bandits with multiple plays"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"
    - "recent trends in deep learning based natural language processing"

Connected through hyperedge HE-5 (induced by source paper "distributed learning for channel allocation over a shared spectrum"):
  with members:
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "game of thrones fully distributed learning for multi player bandits"
    - "proactive resource management for lte in unlicensed spectrum a deep learning perspective"
    - "game theoretic dynamic channel allocation for frequency selective interference channels"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "propagation path loss models for 5g urban micro and macro cellular scenarios"
    - "multi player bandits a musical chairs approach"
    - "distributed channel assignment in cognitive radio networks stable matching and walrasian equilibrium"

Connected through hyperedge HE-6 (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"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "non uniform time step deep q network for carrier sense multiple access in heterogeneous wireless networks"):
  with members:
    - "distributive dynamic spectrum access through deep reinforcement learning a reservoir computing based approach"
    - "carrier sense multiple access for heterogeneous wireless networks using deep reinforcement learning"
    - "actor critic deep reinforcement learning for dynamic multichannel access"
    - "application of machine learning in wireless networks key techniques and open issues"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep reinforcement learning an overview"
    - "proactive resource management for lte in unlicensed spectrum a deep learning perspective"

Connected through hyperedge HE-8 (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"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "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 recurrent q learning for partially observable mdps"

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)