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: "failure analysis of the interval passing algorithm for compressed sensing"
  Abstract: In this work, we perform a complete failure analysis of the interval-passing algorithm (IPA) for compressed sensing, an efficient iterative algorithm for reconstructing a $k$-sparse nonnegative $n$-dimensional real signal $\boldsymbol{x}$ from a small number of linear measurements $\boldsymbol{y}$. In particular, we show that the IPA fails to recover $\boldsymbol{x}$ from $\boldsymbol{y}$ if and only if it fails to recover a corresponding binary vector of the same support, and also that only positions of nonzero values in the measurement matrix are of importance to the success of recovery. Based on this observation, we introduce termatiko sets and show that the IPA fails to fully recover $\boldsymbol x$ if and only if the support of $\boldsymbol x$ contains a nonempty termatiko set, thus giving a complete (graph-theoretic) description of the failing sets of the IPA. Two heuristics to locate small-size termatiko sets are presented. For binary column-regular measurement matrices with no $4$-cycles, we provide a lower bound on the termatiko distance, defined as the smallest size of a nonempty termatiko set. For measurement matrices constructed from the parity-check matrices of array LDPC codes, upper bounds on the termatiko distance are provided for column-weight at most $7$, while for column-weight $3$, the exact termatiko distance and its corresponding multiplicity are provided. Next, we show that adding redundant rows to the measurement matrix does not create new termatiko sets, but rather potentially removes termatiko sets and thus improves performance. An algorithm is provided to efficiently search for such redundant rows. Finally, we present numerical results for different specific measurement matrices and also for protograph-based ensembles of measurement matrices, as well as simulation results of IPA performance, showing the influence of small-size termatiko sets.

Connected through hyperedge HE-1 (induced by source paper "algebraic optimization of binary spatially coupled measurement matrices for interval passing"):
  with members:
    - "a generalized algebraic approach to optimizing sc ldpc codes"
    - "on failing sets of the interval passing algorithm for compressed sensing"
    - "on the minimum distance of array based spatially coupled low density parity check codes"
    - "verification based interval passing algorithm for compressed sensing"
    - "a simple message passing algorithm for compressed sensing"
    - "the effect of spatial coupling on compressive sensing"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "message passing algorithms for compressed sensing i motivation and construction"

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)