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: "on polar polytopes and the recovery of sparse representations"
  Abstract: Suppose we have a signal y which we wish to represent using a linear combination of a number of basis atoms ai,y=Sigmaixiai=Ax. The problem of finding the minimum l0 norm representation for y is a hard problem. The basis pursuit (BP) approach proposes to find the minimum l1 norm representation instead, which corresponds to a linear program (LP) that can be solved using modern LP techniques, and several recent authors have given conditions for the BP (minimum l1 norm) and sparse (minimum l0 norm) representations to be identical. In this paper, we explore this sparse representation problem using the geometry of convex polytopes, as recently introduced into the field by Donoho. By considering the dual LP we find that the so-called polar polytope P* of the centrally symmetric polytope P whose vertices are the atom pairs plusmnai is particularly helpful in providing us with geometrical insight into optimality conditions given by Fuchs and Tropp for non-unit-norm atom sets. In exploring this geometry, we are able to tighten some of these earlier results, showing for example that a condition due to Fuchs is both necessary and sufficient for l1-unique-optimality, and there are cases where orthogonal matching pursuit (OMP) can eventually find all l1-unique-optimal solutions with m nonzeros even if the exact recover condition (ERC) fails for m.

Connected through hyperedge HE-1 (induced by source paper "weak stability of ell_1 minimization methods in sparse data reconstruction"):
  with members:
    - "the gap between the null space property and the restricted isometry property"
    - "1 bit compressive sensing reformulation and rrsp based sign recovery theory"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-2 (induced by source paper "relaxed recovery conditions for omp ols by exploiting both coherence and decay"):
  with members:
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "exact recovery conditions for sparse representations with partial support information"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "sparse vector distributions and recovery from compressed sensing"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "rank awareness in joint sparse recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-3 (induced by source paper "1 bit compressive sensing reformulation and rrsp based sign recovery theory"):
  with members:
    - "blind one bit compressive sampling"
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "rsp based analysis for sparsest and least ell_1 norm solutions to underdetermined linear systems"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing with non gaussian measurements"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-4 (induced by source paper "exact recovery conditions for sparse representations with partial support information"):
  with members:
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "on the theoretical analysis of orthogonal matching pursuit with termination based on the residue"
    - "improved rip analysis of orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "sparse signal reconstruction via iterative support detection"
    - "recovering compressively sampled signals using partial support information"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "a short note on compressed sensing with partially known signal support"

Connected through hyperedge HE-5 (induced by source paper "coherence based partial exact recovery condition for omp ols"):
  with members:
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "improved rip analysis of orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-6 (induced by source paper "convergence analysis of the focuss algorithm"):
  with members:
    - "model based compressive sensing"
    - "blind compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "on the capacity of multiple access z interference channels"):
  with members:
    - "real interference alignment exploiting the potential of single antenna systems"
    - "on a class of discrete memoryless broadcast interference channels"
    - "on interference alignment and the deterministic capacity for cellular channels with weak symmetric cross links"
    - "the approximate capacity of the many to one and one to many gaussian interference channels"
    - "capacity regions and sum rate capacities of vector gaussian interference channels"
    - "on the capacity of the 2 user gaussian mac interfering with a p2p link"
    - "lattice strategies for the dirty multiple access channel"
    - "a layered lattice coding scheme for a class of three user gaussian interference channels"

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)