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: "graph learning from filtered signals graph system and diffusion kernel identification"
  Abstract: This paper introduces a novel graph signal processing framework for building graph-based models from classes of filtered signals. In our framework, graph-based modeling is formulated as a graph system identification problem, where the goal is to learn a weighted graph (a graph Laplacian matrix) and a graph-based filter (a function of graph Laplacian matrices). In order to solve the proposed problem, an algorithm is developed to jointly identify a graph and a graph-based filter (GBF) from multiple signal/data observations. Our algorithm is valid under the assumption that GBFs are one-to-one functions. The proposed approach can be applied to learn diffusion (heat) kernels, which are popular in various fields for modeling diffusion processes. In addition, for specific choices of graph-based filters, the proposed problem reduces to a graph Laplacian estimation problem. Our experimental results demonstrate that the proposed algorithm outperforms the current state-of-the-art methods. We also implement our framework on a real climate dataset for modeling of temperature signals.

Connected through hyperedge HE-1 (induced by source paper "learning graphs from data a signal representation perspective"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "graph learning under sparsity priors"
    - "blind identification of graph filters"
    - "network inference via the time varying graphical lasso"
    - "graph based transform coding with application to image compression"
    - "signal processing on graphs causal modeling of un structured data"
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"

Connected through hyperedge HE-2 (induced by source paper "graph heat mixture model learning"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "graph laplacian mixture model"
    - "graph learning under sparsity priors"
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"
    - "graph learning from data under laplacian and structural constraints"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-3 (induced by source paper "blind community detection from low rank excitations of a graph filter"):
  with members:
    - "latent agents in networks estimation and pricing"
    - "blind identification of graph filters"
    - "autoregressive moving average graph filtering"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "network topology inference from spectral templates"
    - "active sensing of social networks"
    - "learning sparse graphs under smoothness prior"
    - "compressive spectral clustering"

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)