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 graphs from data a signal representation perspective"
  Abstract: The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis, and visualization of structured data. When a natural choice of the graph is not readily available from the data sets, it is thus desirable to infer or learn a graph topology from the data. In this article, we survey solutions to the problem of graph learning, including classical viewpoints from statistics and physics, and more recent approaches that adopt a graph signal processing (GSP) perspective. We further emphasize the conceptual similarities and differences between classical and GSP-based graph-inference methods and highlight the potential advantage of the latter in a number of theoretical and practical scenarios. We conclude with several open issues and challenges that are keys to the design of future signal processing and machine-learning algorithms for learning graphs from data.

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:
    - "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"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "copt coordinated optimal transport on graphs"):
  with members:
    - "graph coarsening with preserved spectral properties"
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "got an optimal transport framework for graph comparison"
    - "solving graph compression via optimal transport"
    - "gromov wasserstein alignment of word embedding spaces"
    - "netlsd hearing the shape of a graph"
    - "approximate nearest neighbor search in high dimensions"
    - "graph reduction with spectral and cut guarantees"

Connected through hyperedge HE-3 (induced by source paper "wasserstein based graph alignment"):
  with members:
    - "on the convergence of adam and beyond"
    - "netlsd hearing the shape of a graph"
    - "learning permutations with sinkhorn policy gradient"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "improved bilinear pooling with cnns"
    - "robust principal component analysis on graphs"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-4 (induced by source paper "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "3d point capsule networks"
    - "weighted multi projection 3d point cloud denoising with estimated tangent planes"
    - "a graph cnn for 3d point cloud classification"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"

Connected through hyperedge HE-5 (induced by source paper "inverse graph learning over optimization networks"):
  with members:
    - "interplay between topology and social learning over weak graphs"
    - "local tomography of large networks under the low observability regime"
    - "consistent tomography under partial observations over adaptive networks"
    - "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"
    - "graph sampling for covariance estimation"

Connected through hyperedge HE-6 (induced by source paper "graph domain adaptation with localized graph signal representations"):
  with members:
    - "deep visual domain adaptation a survey"
    - "unsupervised domain adaptation with copula models"
    - "adversarial discriminative domain adaptation"
    - "geometric deep learning going beyond euclidean data"
    - "joint geometrical and statistical alignment for visual domain adaptation"
    - "deep transfer learning with joint adaptation networks"
    - "learning an invariant hilbert space for domain adaptation"
    - "return of frustratingly easy domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "clustering aware graph construction a joint learning perspective"):
  with members:
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "graph learning from data under laplacian and structural constraints"
    - "learning sparse graphs under smoothness prior"
    - "robust principal component analysis on graphs"
    - "learning laplacian matrix in smooth graph signal representations"
    - "robust and efficient subspace segmentation via least squares regression"

Connected through hyperedge HE-8 (induced by source paper "feature graph learning for 3d point cloud denoising"):
  with members:
    - "graph based blind image deblurring from a single photograph"
    - "weighted multi projection 3d point cloud denoising with estimated tangent planes"
    - "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "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"

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)