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: "compressed sensing for wireless communications useful tips and tricks"
  Abstract: As a paradigm to recover the sparse signal from a small set of linear measurements, compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. However, it is not easy to come up with simple and easy answers to the issues raised while carrying out research on CS. The main purpose of this paper is to provide essential knowledge and useful tips that wireless communication researchers need to know when designing CS-based wireless systems. First, we present an overview of the CS technique, including basic setup, sparse recovery algorithm, and performance guarantee. Then, we describe three distinct subproblems of CS, viz., sparse estimation, support identification, and sparse detection, with various wireless communication applications. We also address main issues encountered in the design of CS-based wireless communication systems. These include potentials and limitations of CS techniques, useful tips that one should be aware of, subtle points that one should pay attention to, and some prior knowledge to achieve better performance. Our hope is that this article will be a useful guide for wireless communication researchers and even non-experts to grasp the gist of CS techniques.

Connected through hyperedge HE-1 (induced by source paper "broadband channel estimation for intelligent reflecting surface aided mmwave massive mimo systems"):
  with members:
    - "closed loop sparse channel estimation for wideband millimeter wave full dimensional mimo systems"
    - "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"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"

Connected through hyperedge HE-2 (induced by source paper "random access for massive machine type communications"):
  with members:
    - "beamwidth control for noma in hybrid mmwave communication systems"
    - "design of non orthogonal beamspace multiple access for cellular internet of things"
    - "on the performance gain of noma over oma in uplink single cell systems"
    - "multi beam noma for hybrid mmwave systems"
    - "joint pilot and payload power control for uplink mimo noma with mrc sic receivers"
    - "reliability based windowed decoding for spatially coupled ldpc codes"
    - "short packet downlink transmission with non orthogonal multiple access"
    - "a multi beam noma framework for hybrid mmwave systems"

Connected through hyperedge HE-3 (induced by source paper "low rank matrix completion a contemporary survey"):
  with members:
    - "generalized sparse and low rank optimization for ultra dense networks"
    - "neural collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "overview of full dimension mimo in lte advanced pro"
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "low rank matrix completion for topological interference management by riemannian pursuit"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "source coding based mmwave channel estimation with deep learning based decoding"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "linear block coding for efficient beam discovery in millimeter wave communication networks"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "joint sparse recovery using signal space matching pursuit"):
  with members:
    - "nearly optimal bounds for orthogonal least squares"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "statistical recovery of simultaneously sparse time varying signals from multiple measurement vectors"
    - "recovery of sparse signals via generalized orthogonal matching pursuit a new analysis"
    - "generalized orthogonal matching pursuit"
    - "subspace methods for joint sparse recovery"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-6 (induced by source paper "study of adaptive activity aware constellation list based detection for massive machine type communications"):
  with members:
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "widely linear precoding for large scale mimo with iqi algorithms and performance analysis"
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "adaptive reduced rank receive processing based on minimum symbol error rate criterion for large scale multiple antenna systems"
    - "robust adaptive beamforming using a low complexity shrinkage based mismatch estimation algorithm"
    - "direction finding algorithms based on joint iterative subspace optimization"
    - "adaptive reduced rank lcmv beamforming algorithms based on joint iterative optimization of filters design and analysis"

Connected through hyperedge HE-7 (induced by source paper "fusion based cooperative support identification for compressive networked sensing"):
  with members:
    - "wireless compressive sensing over fading channels with distributed sparse random projections"
    - "amplitude aided 1 bit compressive sensing over noisy wireless sensor networks"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "recovering compressively sampled signals using partial support information"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-8 (induced by source paper "compressive sensing based channel estimation for millimeter wave full dimensional mimo with lens array"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "compressive sensing techniques for next generation wireless communications"
    - "beamspace channel estimation in mmwave systems via cosparse image reconstruction technique"
    - "multi user millimeter wave mimo with full dimensional lens antenna array"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm"

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)