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: "closed loop sparse channel estimation for wideband millimeter wave full dimensional mimo systems"
  Abstract: This paper proposes a closed-loop sparse channel estimation (CE) scheme for wideband millimeter-wave hybrid full-dimensional multiple-input multiple-output and time division duplexing based systems, which exploits the channel sparsity in both angle and delay domains. At the downlink CE stage, random transmit precoder is designed at base station (BS) for channel sounding, and receive combiners at user devices (UDs) are designed to visualize hybrid array as a low-dimensional digital array for facilitating the multi-dimensional unitary ESPRIT (MDU-ESPRIT) algorithm to estimate respective angle-of-arrivals (AoAs). At the uplink CE stage, the estimated downlink AoAs, namely, uplink angle-of-departures (AoDs), are exploited to design multi-beam transmit precoder at UDs to enable BS to estimate the uplink AoAs, i.e., the downlink AoDs, and delays of different UDs using the MDU-ESPRIT algorithm based on the designed receive combiners at BS. Furthermore, a maximum likelihood approach is proposed to pair the channel parameters acquired at the two stages, and the path gains are then obtained using least squares estimator. According to spectrum estimation theory, our solution can acquire the super-resolution estimations of the AoAs/AoDs and delays of sparse multipath components with low training overhead. Simulation results verify the better CE performance and lower computational complexity of our solution over existing state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "gmd based hybrid beamforming for large reconfigurable intelligent surface assisted millimeter wave massive mimo"):
  with members:
    - "joint active and passive beamforming optimization for intelligent reflecting surface assisted swipt under qos constraints"
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "principal component analysis based broadband hybrid precoding for millimeter wave massive mimo systems"
    - "passive beamforming and information transfer via large intelligent surface"
    - "compressive sensing based channel estimation for millimeter wave full dimensional mimo with lens array"
    - "intelligent reflecting surface enhanced ofdm channel estimation and reflection optimization"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "spatial and frequency wideband effects in millimeter wave massive mimo systems"

Connected through hyperedge HE-2 (induced by source paper "broadband channel estimation for intelligent reflecting surface aided mmwave massive mimo systems"):
  with members:
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "compressive sensing based channel estimation for millimeter wave full dimensional mimo with lens array"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "compressive sensing techniques for next generation wireless communications"
    - "channel estimation for millimeter wave massive mimo with hybrid precoding over frequency selective fading channels"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"

Connected through hyperedge HE-3 (induced by source paper "principal component analysis based broadband hybrid precoding for millimeter wave massive mimo systems"):
  with members:
    - "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"
    - "stochastic successive convex optimization for two timescale hybrid precoding in massive mimo"
    - "machine learning based hybrid precoding for mmwave mimo ofdm with dynamic subarray"

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)