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 parameter estimation in awgn"
  Abstract: Compressed sensing is by now well-established as an effective tool for extracting sparsely distributed information, where sparsity is a discrete concept, referring to the number of dominant nonzero signal components in some basis for the signal space. In this paper, we establish a framework for estimation of continuous-valued parameters based on compressive measurements on a signal corrupted by additive white Gaussian noise (AWGN). While standard compressed sensing based on naive discretization has been shown to suffer from performance loss due to basis mismatch, we demonstrate that this is not an inherent property of compressive measurements. Our contributions are summarized as follows: (a) We identify the isometries required to preserve fundamental estimation-theoretic quantities such as the Ziv-Zakai bound (ZZB) and the Cramer-Rao bound (CRB). Under such isometries, compressive projections can be interpreted simply as a reduction in “effective SNR.” (b) We show that the threshold behavior of the ZZB provides a criterion for determining the minimum number of measurements for “accurate” parameter estimation. (c) We provide detailed computations of the number of measurements needed for the isometries in (a) to hold for the problem of frequency estimation in a mixture of sinusoids. We show via simulations that the design criterion in (b) is accurate for estimating the frequency of a single sinusoid.

Connected through hyperedge HE-1 (induced by source paper "efficient downlink channel reconstruction for fdd multi antenna systems"):
  with members:
    - "multi band covariance interpolation with applications in massive mimo"
    - "channel feedback based on aod adaptive subspace codebook in fdd massive mimo systems"
    - "fdd massive mimo efficient downlink probing and uplink feedback via active channel sparsification"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-2 (induced by source paper "a compressive channel estimation technique robust to synchronization impairments"):
  with members:
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "self calibration and biconvex compressive sensing"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "28 ghz and 73 ghz millimeter wave indoor propagation measurements and path loss models"
    - "compressive sensing of sparse tensors"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "structured compressed sensing from theory to applications"

Connected through hyperedge HE-3 (induced by source paper "joint cfo and channel estimation in millimeter wave systems with one bit adcs"):
  with members:
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "self calibration and biconvex compressive sensing"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "expectation maximization gaussian mixture approximate message passing"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-4 (induced by source paper "newtonized orthogonal matching pursuit frequency estimation over the continuum"):
  with members:
    - "near minimax line spectral estimation"
    - "atomic norm denoising with applications to line spectral estimation"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"
    - "orthogonal matching pursuit with replacement"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-5 (induced by source paper "compressive channel estimation and tracking for large arrays in mm wave picocells"):
  with members:
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "on the feasibility of beamforming in millimeter wave communication systems with multiple antenna arrays"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"

Connected through hyperedge HE-6 (induced by source paper "compressive parameter estimation for sparse translation invariant signals using polar interpolation"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "spectral compressive sensing with polar interpolation"
    - "compressive sensing for spread spectrum receivers"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "noise folding in compressed sensing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-7 (induced by source paper "compressed subspace matching on the continuum"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "matched filtering from limited frequency samples"
    - "near minimax line spectral estimation"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "blind deconvolution using convex programming"
    - "stable manifold embeddings with structured random matrices"
    - "sparse recovery from combined fusion frame measurements"

Connected through hyperedge HE-8 (induced by source paper "channel estimation and hybrid precoding for millimeter wave cellular systems"):
  with members:
    - "spatial compressive sensing for mimo radar"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "near optimal adaptive compressed sensing"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"
    - "analysis of blockage effects on urban cellular networks"
    - "near optimal compressive binary search"
    - "coding the beams improving beamforming training in mmwave communication system"
    - "a tractable approach to coverage and rate in cellular networks"

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)