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 "millimeter wave communications with an intelligent reflector performance optimization and distributional reinforcement learning"):
  Abstract: In this paper, a novel framework is proposed to optimize the downlink multi-user communication of a millimeter wave base station, which is assisted by a reconfigurable intelligent reflector (IR). In particular, a channel estimation approach is developed to measure the channel state information (CSI) in real-time. First, for a perfect CSI scenario, the optimal precoding transmission and power allocation is derived so as to maximize the sum of downlink rates towards multiple users, followed by the optimization of IR reflection coefficient to enhance the upper bound of the downlink transmission. Next, in the imperfect CSI scenario, a distributional reinforcement learning (DRL) approach is proposed to learn the optimal IR reflection and maximize the expectation of downlink capacity. In order to model the transmission rate's probability distribution, a learning algorithm, based on quantile regression (QR), is developed, and the proposed QR-DRL method is proved to converge to a stable distribution of downlink transmission rate. Simulation results show that, in the error-free CSI scenario, the proposed transmission approach yields over 20% and 2-fold increase in the downlink sum-rate, compared with a fixed IR reflection scheme and direct transmission scheme, respectively. Simulation results also show that by increasing the number of IR components, the downlink rate can be improved faster than by increasing the number of antennas at the BS. Furthermore, under limited knowledge of CSI, simulation results show that the proposed QR-DRL method, which learns a full distribution of the downlink rate, yields a better prediction accuracy and improves the downlink rate by 10% for online deployments, compared with a Q-learning baseline.

Includes members:
  Member "intelligent reflecting surface meets ofdm protocol design and rate maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "resource allocation in intelligent reflecting surface assisted noma systems")
  Member "reflections in the sky millimeter wave communication with uav carried intelligent reflectors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting randomly located blockages for large scale deployment of intelligent surfaces")
  Member "reflect and transmit array antennas for scalable and energy efficient mmwave massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the impact of phase shifting designs on irs noma")
  Member "intelligent reflecting and transmitting surface aided millimeter wave massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the impact of phase shifting designs on irs noma")
  Member "a vision of 6g wireless systems applications trends technologies and open research problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "enabling large intelligent surfaces with compressive sensing and deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intelligent reflecting surface assisted secure wireless communications with multiple transmit and multiple receive antennas")
  Member "performance analysis of large intelligent surfaces liss uplink spectral efficiency and pilot training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimizations with intelligent reflecting surfaces irss in 6g wireless networks power control quality of service max min fair beamforming for unicast broadcast and multicast with multi antenna mobile users and multiple irss")
  Member "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "overhead aware design of reconfigurable intelligent surfaces in smart radio environments")

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)