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: "learning heat diffusion graphs"
  Abstract: Effective information analysis generally boils down to properly identifying the structure or geometry of the data, which is often represented by a graph. In some applications, this structure may be partly determined by design constraints or pre-determined sensing arrangements, like in road transportation networks for example. In general though, the data structure is not readily available and becomes pretty difficult to define. In particular, the global smoothness assumptions, that most of the existing works adopt, are often too general and unable to properly capture localized properties of data. In this paper, we go beyond this classical data model and rather propose to represent information as a sparse combination of localized functions that live on a data structure represented by a graph. Based on this model, we focus on the problem of inferring the connectivity that best explains the data samples at different vertices of a graph that is a priori unknown. We concentrate on the case where the observed data is actually the sum of heat diffusion processes, which is a quite common model for data on networks or other irregular structures. We cast a new graph learning problem and solve it with an efficient nonconvex optimization algorithm. Experiments on both synthetic and real world data finally illustrate the benefits of the proposed graph learning framework and confirm that the data structure can be efficiently learned from data observations only. We believe that our algorithm will help solving key questions in diverse application domains such as social and biological network analysis where it is crucial to unveil proper geometry for data understanding and inference.

Connected through hyperedge HE-1 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "graphsac detecting anomalies in large scale graphs"
    - "power systems topology and state estimation by graph blind source separation"
    - "semi blind inference of topologies and dynamical processes over dynamic graphs"
    - "graph signal processing part i graphs graph spectra and spectral clustering"
    - "star gcn stacked and reconstructed graph convolutional networks for recommender systems"
    - "graph signal processing part ii processing and analyzing signals on graphs"
    - "an intuitive derivation of the coherence index relation in compressive sensing"

Connected through hyperedge HE-2 (induced by source paper "learning graphs from data a signal representation perspective"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "graph learning from filtered signals graph system and diffusion kernel identification"
    - "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"

Connected through hyperedge HE-3 (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"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "sampling of graph signals with successive local aggregations"

Connected through hyperedge HE-4 (induced by source paper "network inference from consensus dynamics with unknown parameters"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "signal processing on graphs causal modeling of un structured data"
    - "network inference from consensus dynamics"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"

Connected through hyperedge HE-5 (induced by source paper "graph learning for spatiotemporal signal with long short term characterization"):
  with members:
    - "signal processing on graphs causal modeling of un structured data"
    - "network topology inference from spectral templates"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "learning sparse graphs under smoothness prior"
    - "robust principal component analysis on graphs"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "robust principal component analysis"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (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 from filtered signals graph system and diffusion kernel identification"
    - "graph learning under sparsity priors"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-7 (induced by source paper "graph learning from filtered signals graph system and diffusion kernel identification"):
  with members:
    - "signal processing on graphs causal modeling of un structured data"
    - "network topology inference from spectral templates"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "graph learning from data under laplacian and structural constraints"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "robust subspace clustering"
    - "learning laplacian matrix in smooth graph signal representations"
    - "discrete signal processing on graphs frequency analysis"

Connected through hyperedge HE-8 (induced by source paper "characterization and inference of graph diffusion processes from observations of stationary signals"):
  with members:
    - "graph learning under sparsity priors"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "network scale traffic modeling and forecasting with graphical lasso and neural networks"
    - "graph reconstruction from the observation of diffused signals"
    - "network topology inference from spectral templates"
    - "network topology identification from spectral templates"
    - "learning sparse graphs under smoothness prior"

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)