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 "stochastic successive convex optimization for two timescale hybrid precoding in massive mimo"):
  Abstract: Hybrid precoding, which consists of an RF precoder and a baseband precoder, is a popular precoding architecture for massive multiple-input multiple-output (MIMO) due to its low hardware cost and power consumption. In conventional hybrid precoding, both RF and baseband precoders are adaptive to the real-time channel state information. As a result, an individual RF precoder is required for each subcarrier in wideband systems, leading to high implementation cost. To overcome this issue, two-timescale hybrid precoding (THP), which adapts the RF precoder to the channel statistics, has been proposed. Since the channel statistics are approximately the same over different subcarriers, only a single RF precoder is required in THP. Despite the advantages of THP, there lacks a unified and efficient algorithm for its optimization due to the nonconvex and stochastic nature of the problem. Based on stochastic successive convex approximation (SSCA), we propose an online algorithmic framework called SSCA-THP for general THP optimization problems, in which the hybrid precoder is updated by solving a quadratic surrogate optimization problem whenever a new channel sample is obtained. Then, we prove the convergence of SSCA-THP to stationary points. Finally, we apply SSCA-THP to solve three important THP optimization problems and verify its advantages over existing solutions.

Includes members:
  Member "joint power allocation and user association optimization for massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint power allocation and load balancing optimization for energy efficient cell free massive mimo networks")
  Member "a parallel decomposition method for nonconvex stochastic multi agent optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convergent policy optimization for safe reinforcement learning")
  Member "hybrid digital and analog beamforming design for large scale antenna arrays":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beamforming design for large scale antenna arrays using deep learning")
  Member "hybrid beamforming for massive mimo a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "amplitude and phase estimation for absolute calibration of massive mimo front ends")
  Member "limited feedback hybrid precoding for multi user millimeter wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")
  Member "joint power and antenna selection optimization in large cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "energy efficient downlink transmission for multicell massive das with pilot contamination")
  Member "spatially sparse precoding in millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")
  Member "hierarchical interference mitigation for massive mimo cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave networks")

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)