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 "performance bounds for sparse signal reconstruction with multiple side information"):
  Abstract: In the context of compressive sensing (CS), this paper considers the problem of reconstructing sparse signals with the aid of other given correlated sources as multiple side information (SI). To address this problem, we propose a reconstruction algorithm with multiple SI (RAMSI) that solves a general weighted n-`1 norm minimization. The proposed RAMSI algorithm takes advantage of both CS and the n-`1 minimization by adaptively computing optimal weights among SI signals at every reconstructed iteration. In addition, we establish theoretical performance bounds on the number of measurements that are required to successfully reconstruct the original sparse source using RAMSI under arbitrary support SI conditions. The analyses of the established bounds reveal that RAMSI can achieve sharper bounds and significant performance improvements compared to classical CS. We evaluate experimentally the proposed algorithm and the established bounds using synthetic sparse signals as well as correlated feature histograms, extracted from a multiview image database for object recognition. The obtained results show clearly that the proposed RAMSI algorithm outperforms classical CS and CS with single SI in terms of both the theoretical bounds and the practical performance.

Includes members:
  Member "recursive recovery of sparse signal sequences from compressive measurements a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "real time reconstruction of counting process through queues")
  Member "adaptive rate compressive sensing using side information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "side information in robust principal component analysis algorithms and applications")
  Member "an online algorithm for separating sparse and low dimensional signal sequences from their sum":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provable dynamic robust pca or robust subspace tracking")
  Member "compressed sensing with side information geometrical interpretation and performance bounds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multimodal image super resolution via deep unfolding with side information")
  Member "robust pca with partial subspace knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provable dynamic robust pca or robust subspace tracking")
  Member "living on the edge phase transitions in convex programs with random data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "time invariant error bounds for modified cs based sparse signal sequence recovery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "provable low rank phase retrieval and compressive pca")
  Member "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "minimax optimal online stochastic learning for sequences of convex functions under sub gradient observation failures")

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)