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: "reconstruction of graph signals through percolation from seeding nodes"
  Abstract: New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconstructing bandlimited graph signals, which are signals that admit a sparse representation in a frequency domain related to the structure of the graph. Most existing formulations focus on estimating an  unknown  graph signal by  observing  its value on a subset of nodes. By contrast, in this paper, we study the problem of inducing a  known  graph signal using as input a graph signal that is nonzero only for a small subset of nodes. The sparse signal is then percolated (interpolated) across the graph using a graph filter. Alternatively, one can interpret graph signals as network states and study graph-signal reconstruction as a network-control problem where the target class of states is represented by bandlimited signals. Three setups are investigated. In the first one, a single simultaneous injection takes place on several nodes in the graph. In the second one, successive value injections take place on a single node. The third one is a generalization where multiple nodes inject multiple signal values. For noiseless settings, conditions under which perfect reconstruction is feasible are given, and the corresponding schemes to recover the desired signal are specified. Scenarios leading to imperfect reconstruction, either due to insufficient or noisy signal value injections, are also analyzed. Moreover, connections with classical interpolation in the time domain are discussed. Specifically, for time-varying signals, where the ideal interpolator after uniform sampling  is  a (low-pass) filter, our proposed approach and the reconstruction of a sampled signal coincide. Nevertheless, for general graph signals, we show that these two approaches differ. The last part of the paper presents numerical experiments that illustrate the results developed through synthetic and real-world signal reconstruction problems.

Connected through hyperedge HE-1 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph"):
  with members:
    - "greedy sampling of graph signals"
    - "supervised linear regression for graph learning from graph signals"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "graph sampling for covariance estimation"
    - "kernel based reconstruction of space time functions on dynamic graphs"
    - "network topology inference from spectral templates"
    - "fast robust pca on graphs"

Connected through hyperedge HE-2 (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"
    - "rethinking sketching as sampling a graph signal processing approach"
    - "learning heat diffusion graphs"
    - "sampling of graph signals with successive local aggregations"

Connected through hyperedge HE-3 (induced by source paper "modelling graph errors towards robust graph signal processing"):
  with members:
    - "filtering random graph processes over random time varying graphs"
    - "stationary graph processes and spectral estimation"
    - "graph sampling for covariance estimation"
    - "learning sparse graphs under smoothness prior"
    - "sampling of graph signals with successive local aggregations"
    - "discrete signal processing on graphs sampling theory"
    - "stability and continuity of centrality measures in weighted graphs"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-4 (induced by source paper "fast resampling of three dimensional point clouds via graphs"):
  with members:
    - "greedy sampling of graph signals"
    - "blind identification of graph filters"
    - "convergence analysis of the information matrix in gaussian belief propagation"
    - "graph based compression of dynamic 3d point cloud sequences"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "compressive spectral clustering"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"

Connected through hyperedge HE-5 (induced by source paper "blind community detection from low rank excitations of a graph filter"):
  with members:
    - "latent agents in networks estimation and pricing"
    - "graph learning from filtered signals graph system and diffusion kernel identification"
    - "blind identification of graph filters"
    - "autoregressive moving average graph filtering"
    - "network topology inference from spectral templates"
    - "active sensing of social networks"
    - "learning sparse graphs under smoothness prior"
    - "compressive spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "flow smoothing and denoising graph signal processing in the edge space"):
  with members:
    - "random walks on simplicial complexes and the normalized hodge 1 laplacian"
    - "simplicial closure and higher order link prediction"
    - "autoregressive moving average graph filtering"
    - "hierarchical clustering of asymmetric networks"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "discrete signal processing on graphs sampling theory"
    - "a distributed tracking algorithm for reconstruction of graph signals"

Connected through hyperedge HE-7 (induced by source paper "convolutional neural networks via node varying graph filters"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "multiscale hierarchical convolutional networks"
    - "densely connected convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "sampling of graph signals with successive local aggregations"
    - "hierarchical overlapping clustering of network data using cut metrics"
    - "discrete signal processing on graphs sampling theory"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

Connected through hyperedge HE-8 (induced by source paper "stationary graph processes and spectral estimation"):
  with members:
    - "blind identification of graph filters"
    - "signal processing on graphs causal modeling of un structured data"
    - "stationary signal processing on graphs"
    - "network topology inference from spectral templates"
    - "network topology identification from spectral templates"
    - "subsampling for graph power spectrum estimation"
    - "distributed linear network operators using graph filters"
    - "distributed autoregressive moving average graph filters"

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.SI(Social and Information Networks)