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: "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"
  Abstract: In this paper, we consider the problem of compressive sensing (CS) recovery with a prior support and the prior support quality information available. Different from classical works which exploit prior support blindly, we shall propose novel CS recovery algorithms to exploit the prior support adaptively based on the quality information. We analyze the distortion bound of the recovered signal from the proposed algorithm and we show that a better quality prior support can lead to better CS recovery performance. We also show that the proposed algorithm would converge in   ${\cal O}(\log{\rm SNR})$    steps. To tolerate possible model mismatch, we further propose some robustness designs to combat incorrect prior support quality information. Finally, we apply the proposed framework to sparse channel estimation in massive MIMO systems with temporal correlation to further reduce the required pilot training overhead.

Connected through hyperedge HE-1 (induced by source paper "sparse representation for wireless communications"):
  with members:
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "common codebook millimeter wave beam design designing beams for both sounding and communication with uniform planar arrays"
    - "joint channel training and feedback for fdd massive mimo systems"
    - "compressive sensing based multiuser detector for the large scale sm mimo uplink"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "compressed channel feedback for correlated massive mimo systems"
    - "compressive sensing based differential channel feedback for massive mimo"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-2 (induced by source paper "structured turbo compressed sensing for downlink massive mimo ofdm channel estimation"):
  with members:
    - "denoising based turbo compressed sensing"
    - "approximate message passing with nearest neighbor sparsity pattern learning"
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "fundamental limits of training based multiuser mimo systems"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"

Connected through hyperedge HE-3 (induced by source paper "compressed sensing for wireless communications useful tips and tricks"):
  with members:
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "statistical recovery of simultaneously sparse time varying signals from multiple measurement vectors"

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)