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: "recovery of signals under the high order rip condition via prior support information"
  Abstract: In this paper we study the recovery conditions of weighted $l_{1}$ minimization for signal reconstruction from incomplete linear measurements when partial prior support information is available. We obtain that a high order RIP condition can guarantee stable and robust recovery of signals in bounded $l_{2}$ and Dantzig selector noise settings. Meanwhile, we not only prove that the sufficient recovery condition of weighted $l_{1}$ minimization method is weaker than that of standard $l_{1}$ minimization method, but also prove that weighted $l_{1}$ minimization method provides better upper bounds on the reconstruction error in terms of the measurement noise and the compressibility of the signal, provided that the accuracy of prior support estimate is at least $50\%$. Furthermore, the condition is proved sharp.

Connected through hyperedge HE-1 (induced by source paper "phaseless compressive sensing using partial support information"):
  with members:
    - "recovery of signals by a weighted ell_2 ell_1 minimization under arbitrary prior support information"
    - "recovery analysis for weighted mixed ell_2 ell_p minimization with 0 p leq 1"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "a strong restricted isometry property with an application to phaseless compressed sensing"
    - "phase retrieval for sparse signals"
    - "gespar efficient phase retrieval of sparse signals"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"

Connected through hyperedge HE-2 (induced by source paper "recovery of signals by a weighted ell_2 ell_1 minimization under arbitrary prior support information"):
  with members:
    - "a sharp bound on ric in generalized orthogonal matching pursuit"
    - "weighted ell_1 minimization for sparse recovery under arbitrary prior information"
    - "the high order block rip condition for signal recovery"
    - "a sharp restricted isometry constant bound of 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"
    - "generalized orthogonal matching pursuit"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-3 (induced by source paper "a sharp recovery condition for sparse signals with partial support information via orthogonal matching pursuit"):
  with members:
    - "weighted ell_1 minimization for sparse recovery under arbitrary prior information"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "a sharp condition for exact support recovery of sparse signals with orthogonal matching pursuit"
    - "exact recovery conditions for sparse representations with partial support information"
    - "online recovery guarantees and analytical results for omp"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "on the theoretical analysis of orthogonal matching pursuit with termination based on the residue"
    - "recovering compressively sampled signals using partial support information"

Connected through hyperedge HE-4 (induced by source paper "recovery of signals under the condition on ric and roc via prior support information"):
  with members:
    - "recovery analysis for weighted ell_1 minimization using a null space property"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "time invariant error bounds for modified cs based sparse signal sequence recovery"
    - "compressed sensing and affine rank minimization under restricted isometry"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "weighted ell_1 minimization with multiple weighting sets"
    - "recovering compressively sampled signals using partial support information"
    - "modified cs modifying compressive sensing for problems with partially known support"

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)