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: "exact reconstruction of signals in evolutionary systems via spatiotemporal trade off"
  Abstract: We consider the problem of spatiotemporal sampling in which an initial state \(f\) of an evolution process \(f_t=A_tf\) is to be recovered from a combined set of coarse spatial samples from varying time levels \(\{t_1,\dots ,t_N\}\). This new way of sampling, which we call dynamical sampling, differs from standard sampling since at any fixed time \(t_i\) there are not enough samples to recover the function \(f\) or the state \(f_{t_i}\). Although dynamical sampling is an inverse problem, it differs from the typical inverse problems in which \(f\) is to be recovered from \(A_Tf\) for a single time \(T\). In this paper, we consider signals that are modeled by \(\ell ^2({\mathbb Z})\) or a shift invariant space \(V\subset L^2({\mathbb R})\), and are evolving under the action of a spatial convolution operator \(A\), so that \(f_{n}=A^nf\). We provide sufficient conditions for the spatiotemporal sampling problem to be solvable. In special cases, we provide error analysis based on the spectral properties of the operator \(A\).

Connected through hyperedge HE-1 (induced by source paper "adapted decimation on finite frames for arbitrary orders of sigma delta quantization"):
  with members:
    - "analysis of decimation on finite frames with sigma delta quantization"
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "a deterministic analysis of decimation for sigma delta quantization of bandlimited functions"
    - "noise shaping quantization methods for frame based and compressive sampling systems"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"

Connected through hyperedge HE-2 (induced by source paper "analysis of decimation on finite frames with sigma delta quantization"):
  with members:
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "a deterministic analysis of decimation for sigma delta quantization of bandlimited functions"
    - "noise shaping quantization methods for frame based and compressive sampling systems"

Connected through hyperedge HE-3 (induced by source paper "universal spatiotemporal sampling sets for discrete spatially invariant evolution processes"):
  with members:
    - "system identification in dynamical sampling"
    - "spatially distributed sampling and reconstruction"
    - "discrete sampling and interpolation universal sampling sets for discrete bandlimited spaces"
    - "sparse recovery from combined fusion frame measurements"

Connected through hyperedge HE-4 (induced by source paper "system identification in dynamical sampling"):
  with members:
    - "krylov subspace methods in dynamical sampling"
    - "sampling and reconstruction of signals in a reproducing kernel subspace of lp rd"

Connected through hyperedge HE-5 (induced by source paper "krylov subspace methods in dynamical sampling"):
  with members:
    - "on minimal trajectories for mobile sampling of bandlimited fields"

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.OH(Other Computer Science)