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: "graph convolutional neural networks for web scale recommender systems"
  Abstract: Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains an unsolved challenge. Here we describe a large-scale deep recommendation engine that we developed and deployed at Pinterest. We develop a data-efficient Graph Convolutional Network (GCN) algorithm, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Compared to prior GCN approaches, we develop a novel method based on highly efficient random walks to structure the convolutions and design a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model. We also develop an efficient MapReduce model inference algorithm to generate embeddings using a trained model. Overall, we can train on and embed graphs that are four orders of magnitude larger than typical GCN implementations. We show how GCN embeddings can be used to make high-quality recommendations in various settings at Pinterest, which has a massive underlying graph with 3 billion nodes representing pins and boards, and 17 billion edges. According to offline metrics, user studies, as well as A/B tests, our approach generates higher-quality recommendations than comparable deep learning based systems. To our knowledge, this is by far the largest application of deep graph embeddings to date and paves the way for a new generation of web-scale recommender systems based on graph convolutional architectures.

Connected through hyperedge HE-1 (induced by source paper "a survey on the expressive power of graph neural networks"):
  with members:
    - "random features strengthen graph neural networks"
    - "generalization and representational limits of graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "exact k recommendation via maximal clique optimization"
    - "constant time graph neural networks"
    - "kgat knowledge graph attention network for recommendation"
    - "understanding attention and generalization in graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "advances in collaborative filtering and ranking"):
  with members:
    - "temporal collaborative ranking via personalized transformer"
    - "graph dna deep neighborhood aware graph encoding for collaborative filtering"
    - "stochastic shared embeddings data driven regularization of embedding layers"
    - "hierarchical gating networks for sequential recommendation"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"
    - "recurrent neural networks with top k gains for session based recommendations"

Connected through hyperedge HE-3 (induced by source paper "adargcn adaptive aggregation gcn for few shot learning"):
  with members:
    - "dual graph convolutional network for semantic segmentation"
    - "meta learning with differentiable convex optimization"
    - "infinite mixture prototypes for few shot learning"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "a closer look at few shot classification"

Connected through hyperedge HE-4 (induced by source paper "fastgae fast scalable and effective graph autoencoders with stochastic subgraph decoding"):
  with members:
    - "simple and effective graph autoencoders with one hop linear models"
    - "gravity inspired graph autoencoders for directed link prediction"
    - "a degeneracy framework for scalable graph autoencoders"
    - "effective decoding in graph auto encoder using triadic closure"
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "semi implicit graph variational auto encoders"
    - "improved training speed accuracy and data utilization through loss function optimization"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "strategies for pre training graph neural networks"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "graph warp module an auxiliary module for boosting the power of graph neural networks"
    - "using pre training can improve model robustness and uncertainty"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how powerful are graph neural networks"
    - "pre training graph neural networks with kernels"

Connected through hyperedge HE-6 (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"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "hierarchical graph representation learning with differentiable pooling"
    - "nash toward end to end neural architecture for generative semantic hashing"

Connected through hyperedge HE-7 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction"):
  with members:
    - "contextualized spatial temporal network for taxi origin destination demand prediction"
    - "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "learning semantic specific graph representation for multi label image recognition"
    - "graph to sequence learning using gated graph neural networks"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "hybrid knowledge routed modules for large scale object detection"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"

Connected through hyperedge HE-8 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "graph representation learning via hard and channel wise attention networks"
    - "towards better text understanding and retrieval through kernel entity salience modeling"
    - "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"

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.IR(Information Retrieval)