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: "localized iterative methods for interpolation in graph structured data"
  Abstract: In this paper, we present two localized graph filtering based methods for interpolating graph signals defined on the vertices of arbitrary graphs from only a partial set of samples. The first method is an extension of previous work on reconstructing bandlimited graph signals from partially observed samples. The iterative graph filtering approach very closely approximates the solution proposed in the that work, while being computationally more efficient. As an alternative, we propose a regularization based framework in which we define the cost of reconstruction to be a combination of smoothness of the graph signal and the reconstruction error with respect to the known samples, and find solutions that minimize this cost. We provide both a closed form solution and a computationally efficient iterative solution of the optimization problem. The experimental results on the recommendation system datasets demonstrate effectiveness of the proposed methods.

Connected through hyperedge HE-1 (induced by source paper "semi blind inference of topologies and dynamical processes over dynamic graphs"):
  with members:
    - "stationary graph processes and spectral estimation"
    - "kernel based reconstruction of space time functions on dynamic graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "learning laplacian matrix in smooth graph signal representations"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "clustering partially observed graphs via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "prediction and sampling with local graph transforms for quasi lossless light field compression"):
  with members:
    - "approximate fast graph fourier transforms via multilayer sparse approximations"
    - "graph fourier transform with negative edges for depth image coding"
    - "learning based view synthesis for light field cameras"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-3 (induced by source paper "coding method for parallel iterative linear solver"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coding for distributed fog computing"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "analog coding of a source with erasures"
    - "on the worst case communication overhead for distributed data shuffling"
    - "local set based graph signal reconstruction"

Connected through hyperedge HE-4 (induced by source paper "a sampling theory perspective of graph based semi supervised learning"):
  with members:
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "asymptotic behavior of ell_p based laplacian regularization in semi supervised learning"
    - "the optimal sample complexity of pac learning"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "asymptotic justification of bandlimited interpolation of graph signals for semi supervised learning"
    - "active semi supervised learning using sampling theory for graph signals"
    - "sampling large data on graphs"

Connected through hyperedge HE-5 (induced by source paper "guided signal reconstruction theory"):
  with members:
    - "signals on graphs uncertainty principle and sampling"
    - "guided signal reconstruction with application to image magnification"
    - "discrete signal processing on graphs sampling theory"
    - "signal recovery on graphs fundamental limits of sampling strategies"
    - "active semi supervised learning using sampling theory for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-6 (induced by source paper "distributed adaptive learning of graph signals"):
  with members:
    - "multitask diffusion adaptation over networks with common latent representations"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "adaptive least mean squares estimation of graph signals"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "subsampling for graph power spectrum estimation"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-7 (induced by source paper "adaptive least mean squares estimation of graph signals"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "toward an uncertainty principle for weighted graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "on the degrees of freedom of signals on graphs"
    - "signal recovery on graphs fundamental limits of sampling strategies"
    - "a distributed tracking algorithm for reconstruction of graph signals"

Connected through hyperedge HE-8 (induced by source paper "least mean squares estimation of graph signals"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "toward an uncertainty principle for weighted graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "on the degrees of freedom of signals on graphs"
    - "signal recovery on graphs fundamental limits of sampling strategies"
    - "a distributed tracking algorithm for reconstruction of graph signals"
    - "signal recovery on graphs variation minimization"

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.LG(Machine Learning)