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: "sparse multipath channel estimation using compressive sampling matching pursuit algorithm"
  Abstract: Abstract -Wideband wireless channel is a time dispersive channel and becomes strongly frequency-selective. However, in most cases, the channel is composed of a few dominant taps and a large part of taps is approximately zero or zero. To exploit the sparsity of multi-path channel (MPC), two methods have been proposed. They are, namely, greedy algorithm and convex program. Greedy algorithm is easy to be implemented but not stable; on the other hand, the convex program method is stable but difficult to be implemented as practical channel estimation problems. In this paper, we introduce a novel channel estimation strategy using compressive sampling matching pursuit (CoSaMP) algorithm which was proposed in [1]. This algorithm will combine the greedy algorithm with the convex program method. The effectiveness of the proposed algorithm will be confirmed through comparisons with the existing methods. 0 I. I NTRODUCTION Coherent detection in wideband mobile communication systems often requires accurate channel state information at a receiver. The study of channel estimation for the purposes of channel equalization has a long history. In many studies, densely distributed channel impulse response was often assumed. Under this assumption, it is necessary to use a long training sequence. In addition, the linear channel estimation methods, such as least square (LS) algorithm, always lead to bandwidth inefficiency. It is an interesting study to develop more bandwidth efficient method to acquire channel information. Recently, the compressive sensi ng (CS) has been developed as a new technique. It is regarded as an efficient signal acquisition framework for signals characterized as sparse or compressible in time or frequency domain. One application of the CS technique is in channel estimation. If the channel impulse response follows sparse distribution, we can apply the CS technique. As a result, the training sequence length can be shortened compared with the linear estimation methods. Recent measurements show that the sparse or approximate sparse distribution assumption is reasonable [2, 3]. In other words, the wireless channels in real propagation environments are characterized as sparse or sparse clustered; these sparse or clustered channels are frequently termed as a sparse multi-path channel (SMPC). An example of SMPC impulse response channel is shown in Fig.1. Recently, the study on SMPC has drawn a lot of attentions and concerning results can be found in literature [4-6]. Correspondingl y, sparse channel estimation technique has also received considerable interest for its advantages in high bit rate transmissions over multipath channel [7].

Connected through hyperedge HE-1 (induced by source paper "time domain multi beam selection and its performance improvement for mmwave systems"):
  with members:
    - "hybrid beamforming based on implicit channel state information for millimeter wave links"
    - "frequency selective hybrid beamforming based on implicit csi for millimeter wave systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "spatially sparse precoding in millimeter wave mimo systems"

Connected through hyperedge HE-2 (induced by source paper "compressive sensing based bayesian sparse channel estimation for ofdm communication systems high performance and low complexity"):
  with members:
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "compressive estimation of doubly selective channels in multicarrier systems leakage effects and sparsity enhancing processing"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

Connected through hyperedge HE-3 (induced by source paper "low speed adc sampling based high resolution compressive channel estimation"):
  with members:
    - "segmented compressed sampling for analog to information conversion method and performance analysis"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

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)