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: "sampling and reconstruction of signals in a reproducing kernel subspace of lp rd"
  Abstract: In this paper, we consider sampling and reconstruction of signals in a reproducing kernel subspace of Lp(Rd), 1⩽p⩽∞, associated with an idempotent integral operator whose kernel has certain off-diagonal decay and regularity. The space of p-integrable non-uniform splines and the shift-invariant spaces generated by finitely many localized functions are our model examples of such reproducing kernel subspaces of Lp(Rd). We show that a signal in such reproducing kernel subspaces can be reconstructed in a stable way from its samples taken on a relatively-separated set with sufficiently small gap. We also study the exponential convergence, consistency, and the asymptotic pointwise error estimate of the iterative approximation–projection algorithm and the iterative frame algorithm for reconstructing a signal in those reproducing kernel spaces from its samples with sufficiently small gap.

Connected through hyperedge HE-1 (induced by source paper "adaptive sampling of time space signals in a reproducing kernel subspace of mixed lebesgue space"):
  with members:
    - "sampling based on timing time encoding machines on shift invariant subspaces"

Connected through hyperedge HE-2 (induced by source paper "system identification in dynamical sampling"):
  with members:
    - "exact reconstruction of signals in evolutionary systems via spatiotemporal trade off"
    - "krylov subspace methods in dynamical sampling"

Connected through hyperedge HE-3 (induced by source paper "nonlinear frames and sparse reconstructions in banach spaces"):
  with members:
    - "does ell_p minimization outperform ell_1 minimization"
    - "spatially distributed sampling and reconstruction"
    - "sampling and galerkin reconstruction in reproducing kernel spaces"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "phase retrieval stability and recovery guarantees"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"

Connected through hyperedge HE-4 (induced by source paper "spatially distributed sampling and reconstruction"):
  with members:
    - "sampling and galerkin reconstruction in reproducing kernel spaces"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "sampling and reconstruction of spatial fields using mobile sensors"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-5 (induced by source paper "sampling and galerkin reconstruction in reproducing kernel spaces"):
  with members:
    - "spatially distributed sampling and reconstruction"
    - "sampling based on timing time encoding machines on shift invariant subspaces"
    - "xampling signal acquisition and processing in union of subspaces"

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)