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: "a multiscale pyramid transform for graph signals"
  Abstract: Multiscale transforms designed to process analog and discrete-time signals and images cannot be directly applied to analyze high-dimensional data residing on the vertices of a weighted graph, as they do not capture the intrinsic geometric structure of the underlying graph data domain. In this paper, we adapt the Laplacian pyramid transform for signals on Euclidean domains so that it can be used to analyze high-dimensional data residing on the vertices of a weighted graph. Our approach is to study existing methods and develop new methods for the four fundamental operations of graph downsampling, graph reduction, and filtering and interpolation of signals on graphs. Equipped with appropriate notions of these operations, we leverage the basic multiscale constructs and intuitions from classical signal processing to generate a transform that yields both a multiresolution of graphs and an associated multiresolution of a graph signal on the underlying sequence of graphs.

Connected through hyperedge HE-1 (induced by source paper "copt coordinated optimal transport on graphs"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "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"

Connected through hyperedge HE-3 (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"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "kernel based reconstruction of space time functions on dynamic graphs"
    - "network topology inference from spectral templates"

Connected through hyperedge HE-4 (induced by source paper "unsupervised learning of graph hierarchical abstractions with differentiable coarsening and optimal transport"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "graph u nets"
    - "gromov wasserstein learning for graph matching and node embedding"
    - "self attention graph pooling"
    - "solving graph compression via optimal transport"
    - "provably powerful graph networks"
    - "online planner selection with graph neural networks and adaptive scheduling"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-5 (induced by source paper "hierarchical representation learning in graph neural networks with node decimation pooling"):
  with members:
    - "mincut pooling in graph neural networks"
    - "understanding attention and generalization in graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "towards interpretable sparse graph representation learning with laplacian pooling"
    - "clique pooling for graph classification"
    - "graph u nets"
    - "edge contraction pooling for graph neural networks"
    - "a non negative factorization approach to node pooling in graph convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "graph neural networks with convolutional arma filters"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "on the transferability of spectral graph filters"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "hierarchical graph representation learning with differentiable pooling"
    - "contextual graph markov model a deep and generative approach to graph processing"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-7 (induced by source paper "fractional spectral graph wavelets and their applications"):
  with members:
    - "fractional wavelet scattering network and applications"
    - "approximate fast graph fourier transforms via multilayer sparse approximations"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "robust spatial filtering with graph convolutional neural networks"
    - "geometric deep learning going beyond euclidean data"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-8 (induced by source paper "mincut pooling in graph neural networks"):
  with members:
    - "gap generalizable approximate graph partitioning framework"
    - "graph neural networks with convolutional arma filters"
    - "clique pooling for graph classification"
    - "graph u nets"
    - "a comprehensive survey on graph neural networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "relational inductive biases deep learning and graph networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"

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.IT(Information Theory)