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: "a remark on the restricted isometry property in orthogonal matching pursuit"
  Abstract: This paper demonstrates that if the restricted isometry constant $\delta_{K+1}$ of the measurement matrix $A$ satisfies $$ \delta_{K+1} < \frac{1}{\sqrt{K}+1}, $$ then a greedy algorithm called Orthogonal Matching Pursuit (OMP) can recover every $K$--sparse signal $\mathbf{x}$ in $K$ iterations from $A\x$. By contrast, a matrix is also constructed with the restricted isometry constant $$ \delta_{K+1} = \frac{1}{\sqrt{K}} $$ such that OMP can not recover some $K$-sparse signal $\mathbf{x}$ in $K$ iterations. This result positively verifies the conjecture given by Dai and Milenkovic in 2009.

Connected through hyperedge HE-1 (induced by source paper "progressive stochastic greedy sparse reconstruction and support selection"):
  with members:
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "lazier than lazy greedy"
    - "greedy column subset selection for large scale data sets"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-2 (induced by source paper "basis pursuit and orthogonal matching pursuit for subspace preserving recovery theoretical analysis"):
  with members:
    - "noisy subspace clustering via matching pursuits"
    - "provable self representation based outlier detection in a union of subspaces"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "sparse modeling for image and vision processing"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "greedy feature selection for subspace clustering"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"

Connected through hyperedge HE-3 (induced by source paper "when does omp achieve support recovery with continuous dictionaries"):
  with members:
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "super resolution of positive sources the discrete setup"
    - "robust recovery of positive stream of pulses"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "towards a mathematical theory of super resolution"
    - "improved rip analysis of orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "learning gaussian mixtures with arbitrary separation"

Connected through hyperedge HE-4 (induced by source paper "stochastic greedy closing the optimality gap in exact weak submodular maximization"):
  with members:
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "lazier than lazy greedy"
    - "active semi supervised learning using submodular functions"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-5 (induced by source paper "exact sparse signal recovery via orthogonal matching pursuit with prior information"):
  with members:
    - "sparse activity detection for massive connectivity"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-6 (induced by source paper "super resolution mmwave channel estimation using atomic norm minimization"):
  with members:
    - "adaptive interference removal for uncoordinated radar communication coexistence"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "hybrid mimo architectures for millimeter wave communications phase shifters or switches"
    - "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"
    - "millimeter wave channel modeling and cellular capacity evaluation"
    - "compressed sensing off the grid"

Connected through hyperedge HE-7 (induced by source paper "a sharp bound on ric in generalized orthogonal matching pursuit"):
  with members:
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "improving the bound on the rip constant in generalized orthogonal matching pursuit"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "the performance of orthogonal multi matching pursuit under rip"
    - "generalized orthogonal matching pursuit"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-8 (induced by source paper "a sharp recovery condition for block sparse signals by block orthogonal multi matching pursuit"):
  with members:
    - "a sharp bound on ric in generalized orthogonal matching pursuit"
    - "improved sufficient conditions for sparse recovery with generalized orthogonal matching pursuit"
    - "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "the performance of orthogonal multi matching pursuit under rip"
    - "generalized orthogonal matching pursuit"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "subspace pursuit for compressive sensing signal reconstruction"

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)