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 "analysis of sparse representations using bi orthogonal dictionaries"):
  Abstract: The sparse representation problem of recovering an N dimensional sparse vector x from M &#60; N linear observations y = Dx given dictionary D is considered. The standard approach is to let the elements of the dictionary be independent and identically distributed (IID) zero-mean Gaussian and minimize the l 1 -norm of x under the constraint y = Dx. In this paper, the performance of l 1 -reconstruction is analyzed, when the dictionary is bi-orthogonal D = [O 1  O 2 ], where O 1 , O 2  are independent and drawn uniformly according to the Haar measure on the group of orthogonal M × M matrices. By an application of the replica method, we obtain the critical conditions under which perfect l 1 -recovery is possible with bi-orthogonal dictionaries.

Includes members:
  Member "support recovery with sparsely sampled free random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "typical l 1 recovery limit of sparse vectors represented by concatenations of random orthogonal matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing")
  Member "asymptotic analysis of map estimation via the replica method and applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "optimal incorporation of sparsity information by weighted l 1 optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rls recovery with asymmetric penalty fundamental limits and algorithmic approaches")
  Member "statistical mechanical analysis of compressed sensing utilizing correlated compression matrix":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressed sensing performance analysis via replica method using bayesian framework")
  Member "a typical reconstruction limit of compressed sensing based on lp norm minimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "understanding overfitting peaks in generalization error analytical risk curves for l 2 and l 1 penalized interpolation")
  Member "message passing algorithms for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")

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)