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: "on the restricted isometry of the columnwise khatri rao product"
  Abstract: The columnwise Khatri–Rao product of two matrices is an important matrix type, reprising its role as a structured sensing matrix in many fundamental linear inverse problems. Robust signal recovery in such inverse problems is often contingent on proving the restricted isometry property (RIP) of a certain system matrix expressible as a Khatri–Rao product of two matrices. In this paper, we analyze the RIP of a generic columnwise Khatri–Rao product matrix by deriving two upper bounds for its   $k$   th order restricted isometry constant (  $k$   -RIC) for different values of   $k$   . The first RIC bound is computed in terms of the individual RICs of the real-valued input matrices participating in the Khatri–Rao product. The second RIC bound is probabilistic and is specified in terms of the input matrix dimensions. We show that the Khatri–Rao product of a pair of    $m \times n$   sized random matrices comprising independent and identically distributed sub-Gaussian entries satisfies   $k$   -RIP with arbitrarily high probability, provided   $m$    exceeds   $\mathcal {O}(\sqrt{k}\log ^{3/2}{n})$   . This is a substantially milder condition compared to    $\mathcal {O}(k\log {n})$   rows needed to guarantee   $k$  -RIP of the input sub-Gaussian random matrices participating in the Khatri–Rao product. Our RIC bounds confirm that the Khatri–Rao product exhibits stronger restricted isometry compared to its constituent matrices for the same RIP order. The proposed RIC bounds are potentially useful in obtaining improved performance guarantees in several sparse signal recovery and tensor decomposition problems.

Connected through hyperedge HE-1 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"):
  with members:
    - "improved scaling law for activity detection in massive mimo systems"
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"

Connected through hyperedge HE-2 (induced by source paper "grant free massive random access with a massive mimo receiver"):
  with members:
    - "phase transition analysis for covariance based massive random access with massive mimo"
    - "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"
    - "on the restricted isometry property of centered self khatri rao products"
    - "polar coding and random spreading for unsourced multiple access"
    - "an enhanced decoding algorithm for coded compressed sensing"
    - "improved scaling law for activity detection in massive mimo systems"
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "sparse activity detection for massive connectivity"

Connected through hyperedge HE-3 (induced by source paper "on the restricted isometry property of centered self khatri rao products"):
  with members:
    - "massive mimo unsourced random access"
    - "improved scaling law for activity detection in massive mimo systems"
    - "multiple measurement vectors problem a decoupling property and its applications"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"

Connected through hyperedge HE-4 (induced by source paper "on the support recovery of jointly sparse gaussian sources using sparse bayesian learning"):
  with members:
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "estimation of sparse mimo channels with common support"
    - "subspace methods for joint sparse recovery"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "rank awareness in joint sparse recovery"
    - "distributed compressive sensing"
    - "performance analysis for sparse support recovery"

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)