human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "recovery of signals under the condition on ric and roc via prior support information"):
  Abstract: In this paper, the sufficient condition in terms of the RIC and ROC for the stable and robust recovery of signals in both noiseless and noisy settings was established via weighted $l_{1}$ minimization when there is partial prior information on support of signals. An improved performance guarantee has been derived. #R##N#We can obtain a less restricted sufficient condition for signal reconstruction and a tighter recovery error bound under some conditions via weighted $l_{1}$ minimization. When prior support estimate is at least $50\%$ accurate, the sufficient condition is weaker than the analogous condition by standard $l_{1}$ minimization method, meanwhile the reconstruction error upper bound is provably to be smaller under additional conditions. Furthermore, the sufficient condition is also proved sharp.

Includes members:
  Member "recovery of signals under the high order rip condition via prior support information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "phaseless compressive sensing using partial support information")
  Member "recovery analysis for weighted ell_1 minimization using a null space property":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weighted ell_1 minimization for sparse recovery under arbitrary prior information")
  Member "sparse representation of a polytope and recovery of sparse signals and low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "time invariant error bounds for modified cs based sparse signal sequence recovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provable low rank phase retrieval and compressive pca")
  Member "compressed sensing and affine rank minimization under restricted isometry":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "truncated sparse approximation property and truncated q norm minimization")
  Member "sharp rip bound for sparse signal and low rank matrix recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "weighted ell_1 minimization with multiple weighting sets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weighted ell_1 minimization for sparse recovery under arbitrary prior information")
  Member "recovering compressively sampled signals using partial support information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploiting prior information in block sparse signals")

Question: Please tell me which class the source paper 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)