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: "spatially distributed sampling and reconstruction"
  Abstract: A spatially distributed system contains a large amount of agents with limited sensing, data processing, and communication capabilities. Recent technological advances have opened up possibilities to deploy spatially distributed systems for signal sampling and reconstruction. In this paper, we introduce a graph structure for a distributed sampling and reconstruction system by coupling agents in a spatially distributed system with innovative positions of signals. A fundamental problem in sampling theory is the robustness of signal reconstruction in the presence of sampling noises. For a distributed sampling and reconstruction system, the robustness could be reduced to the stability of its sensing matrix. In a traditional centralized sampling and reconstruction system, the stability of the sensing matrix could be verified by its central processor, but the above procedure is infeasible in a distributed sampling and reconstruction system as it is decentralized. In this paper, we split a distributed sampling and reconstruction system into a family of overlapping smaller subsystems, and we show that the stability of the sensing matrix holds if and only if its quasi-restrictions to those subsystems have uniform stability. This new stability criterion could be pivotal for the design of a robust distributed sampling and reconstruction system against supplement, replacement and impairment of agents, as we only need to check the uniform stability of affected subsystems. In this paper, we also propose an exponentially convergent distributed algorithm for signal reconstruction, that provides a suboptimal approximation to the original signal in the presence of bounded sampling noises.

Connected through hyperedge HE-1 (induced by source paper "stable phaseless sampling and reconstruction of real valued signals with finite rate of innovations"):
  with members:
    - "phaseless sampling and reconstruction of real valued signals in shift invariant spaces"
    - "phase retrieval of real valued signals in a shift invariant space"
    - "phase retrieval an overview of recent developments"
    - "phase retrieval for sparse signals"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-2 (induced by source paper "phaseless sampling and reconstruction of real valued signals in shift invariant spaces"):
  with members:
    - "phase retrieval from multiple window short time fourier measurements"
    - "phase retrieval from the magnitudes of affine linear measurements"
    - "corruption robust phase retrieval via linear programming"
    - "phase retrieval of real valued signals in a shift invariant space"
    - "phase retrieval an overview of recent developments"
    - "phaseless signal recovery in infinite dimensional spaces using structured modulations"
    - "phase retrieval from low rate samples"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-3 (induced by source paper "nonsubsampled graph filter banks and distributed implementation"):
  with members:
    - "discrete signal processing on graphs frequency analysis"
    - "a multiscale pyramid transform for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"
    - "compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs"
    - "perfect reconstruction two channel wavelet filter banks for graph structured data"
    - "transform based distributed data gathering"

Connected through hyperedge HE-4 (induced by source paper "universal spatiotemporal sampling sets for discrete spatially invariant evolution processes"):
  with members:
    - "system identification in dynamical sampling"
    - "exact reconstruction of signals in evolutionary systems via spatiotemporal trade off"
    - "discrete sampling and interpolation universal sampling sets for discrete bandlimited spaces"
    - "sparse recovery from combined fusion frame measurements"

Connected through hyperedge HE-5 (induced by source paper "phase retrieval of real valued signals in a shift invariant space"):
  with members:
    - "phaseless sampling and reconstruction of real valued signals in shift invariant spaces"
    - "phase retrieval from the magnitudes of affine linear measurements"
    - "prime phase retrieval via majorization minimization"
    - "nonlinear frames and sparse reconstructions in banach spaces"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phase retrieval an overview of recent developments"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "phase retrieval via structured modulations in paley wiener spaces"

Connected through hyperedge HE-6 (induced by source paper "nonlinear frames and sparse reconstructions in banach spaces"):
  with members:
    - "does ell_p minimization outperform ell_1 minimization"
    - "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"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-7 (induced by source paper "sampling and galerkin reconstruction in reproducing kernel spaces"):
  with members:
    - "sampling based on timing time encoding machines on shift invariant subspaces"
    - "xampling signal acquisition and processing in union of subspaces"
    - "sampling and reconstruction of signals in a reproducing kernel subspace of lp rd"

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)