human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep reinforcement learning for 5g networks joint beamforming power control and interference coordination"):
  Abstract: The fifth generation of wireless communications (5G) promises massive increases in traffic volume and data rates, as well as improved reliability in voice calls. Jointly optimizing beamforming, power control, and interference coordination in a 5G wireless network to enhance the communication performance to end users poses a significant challenge. In this paper, we formulate the joint design of beamforming, power control, and interference coordination to maximize the signal to interference plus noise ratio (SINR) and solve the non-convex problem using deep reinforcement learning. By using the greedy nature of deep Q-learning to estimate future benefits of actions, we propose an algorithm for voice bearers in sub-6 GHz bands and data bearers in millimeter wave (mmWave) frequency bands. The algorithm exploits reported SINR from connected users, the transmit powers of the base stations, and the coordinates of the connected users to improve the performance measured by coverage and sumrate capacity. The proposed algorithm does not require the channel state information and removes the need for channel estimation. Simulation results show that our algorithm outperforms the link adaptation industry standards for sub-6 GHz voice bearers and approaches the optimal limits for mmWave data bearers for small antenna sizes in realistic cellular environments.

Includes members:
  Member "a deep learning framework for optimization of miso downlink beamforming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration")
  Member "deep learning for tdd and fdd massive mimo mapping channels in space and frequency":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "a framework for automated cellular network tuning with reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "deep learning in downlink coordinated multipoint in new radio heterogeneous networks":
    also connected through other hyperedges:
      (none)
  Member "q learning algorithm for volte closed loop power control in indoor small cells":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a framework for automated cellular network tuning with reinforcement learning")
  Member "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "swift link a compressive beam alignment algorithm for practical mmwave radios")
  Member "deep learning coordinated beamforming for highly mobile millimeter wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "deep reinforcement learning for dynamic multichannel access in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")

Question: Please tell me which class the source paper 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)