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: "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.

Connected through hyperedge HE-1 (induced by source paper "principal component analysis based broadband hybrid precoding for millimeter wave massive mimo systems"):
  with members:
    - "closed loop sparse channel estimation for wideband millimeter wave full dimensional mimo systems"
    - "periodic analog channel estimation aided beamforming for massive mimo systems"
    - "device to device communications enabled energy efficient multicast scheduling in mmwave small cells"
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "wideband hybrid precoding for next generation backhaul fronthaul based on mmwave fd mimo"
    - "hybrid beamforming with selection for multiuser massive mimo systems"
    - "massive mimo channel estimation for millimeter wave systems via matrix completion"
    - "machine learning based hybrid precoding for mmwave mimo ofdm with dynamic subarray"

Connected through hyperedge HE-2 (induced by source paper "joint design of fronthauling and hybrid beamforming for downlink c ran systems"):
  with members:
    - "channel diagonalization for cloud radio access"
    - "energy efficiency maximization for c rans discrete monotonic optimization penalty and ell _ 0 approximation methods"
    - "hybrid analog digital beamforming for massive mimo systems"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "hybrid precoding for massive mimo systems in cloud ran architecture with capacity limited fronthauls"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "a generalized framework on beamformer design and csi acquisition for single carrier massive mimo systems in millimeter wave channels"

Connected through hyperedge HE-3 (induced by source paper "closed loop sparse channel estimation for wideband millimeter wave full dimensional mimo systems"):
  with members:
    - "wideband hybrid precoding for next generation backhaul fronthaul based on mmwave fd mimo"
    - "over sampling codebook based hybrid minimum sum mean square error precoding for millimeter wave 3d mimo"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "compressive sensing techniques for next generation wireless communications"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "hierarchical codebook design for beamforming training in millimeter wave communication"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

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.IT(Information Theory)