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 "exploiting prior information in block sparse signals"):
  Abstract: We study the problem of recovering a block-sparse signal from under-sampled observations. The non-zero values of such signals appear in few blocks, and their recovery is often accomplished using an   $\ell _{1,2}$   optimization problem. In applications such as DNA micro-arrays, some extra information about the distribution of non-zero blocks is available; i.e., the number of non-zero blocks in certain subsets of the blocks is known. A typical way to consider the extra information in recovery procedures is to solve a weighted   $\ell _{1,2}$   problem. In this paper, we consider a block-sparse model which is accompanied with a partitioning of the blocks; besides the overall block-sparsity level of the signal, we assume to know the block-sparsity of each subset in the partition. Our goal in this work is to minimize the number of required linear measurements for perfect recovery of the signal by tuning the weights of a weighted   $\ell _{1,2}$   problem. For this goal, we apply tools from conic integral geometry and derive closed-form expressions for the optimal weights. We show through precise analysis and simulations that the weighted   $\ell _{1,2}$   problem with optimal weights significantly outperforms the regular   $\ell _{1,2}$   problem. We further show that the optimal weights are robust against the inaccuracies of prior information.

Includes members:
  Member "maximum a posteriori signal recovery with prior information applications to compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rls based detection for massive spatial modulation mimo")
  Member "performance limits for noisy multimeasurement vector problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the committee machine computational to statistical gaps in learning a two layers neural network")
  Member "convex recovery of a structured signal from independent random linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "spectral super resolution with prior knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi dimensional spectral super resolution with prior knowledge via frequency selective vandermonde decomposition and admm")
  Member "living on the edge phase transitions in convex programs with random data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "living near the edge a lower bound on the phase transition of total variation minimization")
  Member "accelerating multi echo t2 weighted mr imaging analysis prior group sparse optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "how to exploit prior information in low complexity models")
  Member "recovering compressively sampled signals using partial support information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deterministic analysis of weighted bpdn with partially known support information")

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)