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: "rethinking sketching as sampling a graph signal processing approach"
  Abstract: Sampling of signals belonging to a low-dimensional subspace has well-documented merits for dimensionality reduction, limited memory storage, and online processing of streaming network data. When the subspace is known, these signals can be modeled as bandlimited graph signals. Most existing sampling methods are designed to minimize the error incurred when reconstructing the original signal from its samples. Oftentimes these parsimonious signals serve as inputs to computationally-intensive linear operators. Hence, interest shifts from reconstructing the signal itself towards approximating the output of the prescribed linear operator efficiently. In this context, we propose a novel sampling scheme that leverages graph signal processing, exploiting the low-dimensional (bandlimited) structure of the input as well as the transformation whose output we wish to approximate. We formulate problems to jointly optimize sample selection and a sketch of the target linear transformation, so when the latter is applied to the sampled input signal the result is close to the desired output. Similar sketching as sampling ideas are also shown effective in the context of linear inverse problems. Because these designs are carried out off line, the resulting sampling plus reduced-complexity processing pipeline is particularly useful for data that are acquired or processed in a sequential fashion, where the linear operator has to be applied fast and repeatedly to successive inputs or response signals. Numerical tests showing the effectiveness of the proposed algorithms include classification of handwritten digits from as few as 20 out of 784 pixels in the input images and selection of sensors from a network deployed to carry out a distributed parameter estimation task.

Connected through hyperedge HE-1 (induced by source paper "controllability of bandlimited graph processes over random time varying graphs"):
  with members:
    - "localized linear regression in networked data"
    - "greedy sampling of graph signals"
    - "adaptive graph signal processing algorithms and optimal sampling strategies"
    - "autoregressive moving average graph filtering"
    - "filtering random graph processes over random time varying graphs"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "learning heat diffusion graphs"
    - "sampling of graph signals with successive local aggregations"

Connected through hyperedge HE-2 (induced by source paper "adaptive graph signal processing algorithms and optimal sampling strategies"):
  with members:
    - "greedy sampling of graph signals"
    - "autoregressive moving average graph filtering"
    - "kernel based reconstruction of space time functions on dynamic graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "adaptive least mean squares estimation of graph signals"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "local set based graph signal reconstruction"

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)