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: "system identification in dynamical sampling"
  Abstract: We consider the problem of spatiotemporal sampling in a discrete infinite dimensional spatially invariant evolutionary process x(n) = Anx to recover an unknown convolution operator A given by a filter a?l1(?)$a \in \ell ^{1}(\mathbb {Z})$ and an unknown initial state x modeled as a vector in l2(?)$\ell ^{2}(\mathbb {Z})$. Traditionally, under appropriate hypotheses, any x can be recovered from its samples on ?$\mathbb {Z}$ and A can be recovered by the classical techniques of deconvolution. In this paper, we will exploit the spatiotemporal correlation and propose a new sampling scheme to recover A and x that allows us to sample the evolving states x,Ax,? ,AN?1x on a sub-lattice of ?$\mathbb {Z}$, and thus achieve a spatiotemporal trade off. The spatiotemporal trade off is motivated by several industrial applications (Lu and Vetterli, 2249---2252, 2009). Specifically, we show that {x(m?),Ax(m?),?,AN?1x(m?):N?2m}$\{x(m\mathbb {Z}), Ax(m\mathbb {Z}), \cdots , A^{N-1}x(m\mathbb {Z}): N \geq 2m\}$ contains enough information to recover a typical "low pass filter" a and x almost surely, thus generalizing the idea of the finite dimensional case in Aldroubi and Krishtal, arXiv:1412.1538 (2014). In particular, we provide an algorithm based on a generalized Prony method for the case when both a and x are of finite impulse response and an upper bound of their support is known. We also perform a perturbation analysis based on the spectral properties of the operator A and initial state x, and verify the results by several numerical experiments. Finally, we provide several other numerical techniques to stabilize the proposed method, with some examples to demonstrate the improvement.

Connected through hyperedge HE-1 (induced by source paper "universal spatiotemporal sampling sets for discrete spatially invariant evolution processes"):
  with members:
    - "exact reconstruction of signals in evolutionary systems via spatiotemporal trade off"
    - "spatially distributed sampling and reconstruction"
    - "discrete sampling and interpolation universal sampling sets for discrete bandlimited spaces"
    - "sparse recovery from combined fusion frame measurements"

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)