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: "hierarchical graph representation learning with differentiable pooling"
  Abstract: Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods are inherently flat and do not learn hierarchical representations of graphs---a limitation that is especially problematic for the task of graph classification, where the goal is to predict the label associated with an entire graph. Here we propose DiffPool, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer. Our experimental results show that combining existing GNN methods with DiffPool yields an average improvement of 5-10% accuracy on graph classification benchmarks, compared to all existing pooling approaches, achieving a new state-of-the-art on four out of five benchmark data sets.

Connected through hyperedge HE-1 (induced by source paper "memory based graph networks"):
  with members:
    - "mincut pooling in graph neural networks"
    - "clique pooling for graph classification"
    - "graph u nets"
    - "generating diverse high fidelity images with vq vae 2"
    - "relational graph representation learning for open domain question answering"
    - "self attention graph pooling"
    - "unsupervised multi task feature learning on point clouds"
    - "large memory layers with product keys"

Connected through hyperedge HE-2 (induced by source paper "benchmarking graph neural networks"):
  with members:
    - "understanding attention and generalization in graph neural networks"
    - "a fair comparison of graph neural networks for graph classification"
    - "are powerful graph neural nets necessary a dissection on graph classification"
    - "a two step graph convolutional decoder for molecule generation"
    - "on graph classification networks datasets and baselines"
    - "self attention graph pooling"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "an efficient graph convolutional network technique for the travelling salesman problem"

Connected through hyperedge HE-3 (induced by source paper "hierarchical generation of molecular graphs using structural motifs"):
  with members:
    - "discrete object generation with reversible inductive construction"
    - "graph u nets"
    - "conditional molecular design with deep generative models"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "optimization of molecules via deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "learning to hash with graph neural networks for recommender systems"):
  with members:
    - "is a single vector enough exploring node polysemy for network embedding"
    - "deep representation learning for social network analysis"
    - "graph neural networks for social recommendation"
    - "neural graph collaborative filtering"
    - "deep learning based recommender system a survey and new perspectives"
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "nash toward end to end neural architecture for generative semantic hashing"

Connected through hyperedge HE-5 (induced by source paper "taxoexpan self supervised taxonomy expansion with position enhanced graph neural network"):
  with members:
    - "a user centered concept mining system for query and document understanding at tencent"
    - "every child should have parents a taxonomy refinement algorithm based on hyperbolic term embeddings"
    - "position aware graph neural networks"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "hiexpan task guided taxonomy construction by hierarchical tree expansion"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hearst patterns revisited automatic hypernym detection from large text corpora"

Connected through hyperedge HE-6 (induced by source paper "permutohedral gcn graph convolutional networks with global attention"):
  with members:
    - "reformer the efficient transformer"
    - "geom gcn geometric graph convolutional networks"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "permutohedral attention module for efficient non local neural networks"
    - "learning task specific generalized convolutions in the permutohedral lattice"
    - "graph u nets"
    - "position aware graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-7 (induced by source paper "a deep generative model for graph layout"):
  with members:
    - "evaluating the readability of force directed graph layouts a deep learning approach"
    - "on the convergence of adam and beyond"
    - "aesthetic discrimination of graph layouts"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "representation learning on graphs with jumping knowledge networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "automatic chemical design using a data driven continuous representation of molecules"

Connected through hyperedge HE-8 (induced by source paper "fast detection of maximum common subgraph via deep q learning"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "learning combinatorial embedding networks for deep graph matching"
    - "learning to route in similarity graphs"
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "gromov wasserstein learning for graph matching and node embedding"
    - "a learning based branch and bound for maximum common subgraph problems"
    - "fast graph representation learning with pytorch geometric"

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)