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: "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
  Abstract: Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mixing relationships. To address this weakness, we propose a new model, MixHop, that can learn these relationships, including difference operators, by repeatedly mixing feature representations of neighbors at various distances. Mixhop requires no additional memory or computational complexity, and outperforms on challenging baselines. In addition, we propose sparsity regularization that allows us to visualize how the network prioritizes neighborhood information across different graph datasets. Our analysis of the learned architectures reveals that neighborhood mixing varies per datasets.

Connected through hyperedge HE-1 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks"):
  with members:
    - "anti money laundering in bitcoin experimenting with graph convolutional networks for financial forensics"
    - "graph neural networks with convolutional arma filters"
    - "a fair comparison of graph neural networks for graph classification"
    - "what graph neural networks cannot learn depth vs width"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "simplifying graph convolutional networks"
    - "learning individual treatment effects from networked observational data"
    - "explainability techniques for graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions"):
  with members:
    - "graph representation learning via hard and channel wise attention networks"
    - "towards better text understanding and retrieval through kernel entity salience modeling"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hypergraph neural networks"
    - "graph convolutional networks for text classification"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "deepinf social influence prediction with deep learning"
    - "learning structural node embeddings via diffusion wavelets"

Connected through hyperedge HE-3 (induced by source paper "ripple walk training a subgraph based training framework for large and deep graph neural network"):
  with members:
    - "the truly deep graph convolutional networks for node classification"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "pairnorm tackling oversmoothing in gnns"
    - "graphsaint graph sampling based inductive learning method"
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "graph star net for generalized multi task learning"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-4 (induced by source paper "graph neighborhood attentive pooling"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "simplifying graph convolutional networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "diffusion maps for textual network embedding"
    - "attentive pooling networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-5 (induced by source paper "scattering gcn overcoming oversmoothness in graph convolutional networks"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "spectral multigraph networks for discovering and fusing relationships in molecules"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "diffusion scattering transforms on graphs"
    - "geometric scattering for graph data analysis"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "higher order label homogeneity and spreading in graphs"):
  with members:
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "higher order ranking and link prediction from closing triangles to closing higher order motifs"
    - "higher order clustering in networks"
    - "simplicial closure and higher order link prediction"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-7 (induced by source paper "neural message passing on high order paths"):
  with members:
    - "directional message passing for molecular graphs"
    - "pan path integral based convolution for deep graph neural networks"
    - "path augmented graph transformer network"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-8 (induced by source paper "diffusion improves graph learning"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "simplifying graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "optimizing generalized pagerank methods for seed expansion community detection"
    - "node embedding with adaptive similarities for scalable learning over graphs"
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"

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)