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 "reconfigurable intelligent surface assisted multiuser miso systems exploiting deep reinforcement learning"):
  Abstract: Recently, the reconfigurable intelligent surface (RIS), benefited from the breakthrough on the fabrication of programmable meta-material, has been speculated as one of the key enabling technologies for the future six generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology to achieve smart radio environments. Employed as reflecting arrays, RIS is able to assist MIMO transmissions without the need of radio frequency chains resulting in considerable reduction in power consumption. In this paper, we investigate the joint design of transmit beamforming matrix at the base station and the phase shift matrix at the RIS, by leveraging recent advances in deep reinforcement learning (DRL). We first develop a DRL based algorithm, in which the joint design is obtained through trial-and-error interactions with the environment by observing predefined rewards, in the context of continuous state and action. Unlike the most reported works utilizing the alternating optimization techniques to alternatively obtain the transmit beamforming and phase shifts, the proposed DRL based algorithm obtains the joint design simultaneously as the output of the DRL neural network. Simulation results show that the proposed algorithm is not only able to learn from the environment and gradually improve its behavior, but also obtains the comparable performance compared with two state-of-the-art benchmarks. It is also observed that, appropriate neural network parameter settings will improve significantly the performance and convergence rate of the proposed algorithm.

Includes members:
  Member "beamforming design for large scale antenna arrays using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised learning for passive beamforming")
  Member "indoor signal focusing with deep learning designed reconfigurable intelligent surfaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "intelligent reflecting surface assisted secure wireless communications with multiple transmit and multiple receive antennas")
  Member "learning to optimize training deep neural networks for interference management":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "achievable rate maximization by passive intelligent mirrors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intelligent reflecting surface assisted secure wireless communications with multiple transmit and multiple receive antennas")
  Member "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overhead aware design of reconfigurable intelligent surfaces in smart radio environments")
  Member "beyond massive mimo the potential of positioning with large intelligent surfaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parallel factor decomposition channel estimation in ris assisted multi user miso communication")
  Member "beyond massive mimo the potential of data transmission with large intelligent surfaces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "overhead aware design of reconfigurable intelligent surfaces in smart radio environments")
  Member "applications of deep reinforcement learning in communications and networking a survey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")

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.IT(Information Theory)