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: "star gcn stacked and reconstructed graph convolutional networks for recommender systems"
  Abstract: We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario. STAR-GCN employs a stack of GCN encoder-decoders combined with intermediate supervision to improve the final prediction performance. Unlike the graph convolutional matrix completion model with one-hot encoding node inputs, our STAR-GCN learns low-dimensional user and item latent factors as the input to restrain the model space complexity. Moreover, our STAR-GCN can produce node embeddings for new nodes by reconstructing masked input node embeddings, which essentially tackles the cold start problem. Furthermore, we discover a label leakage issue when training GCN-based models for link prediction tasks and propose a training strategy to avoid the issue. Empirical results on multiple rating prediction benchmarks demonstrate our model achieves state-of-the-art performance in four out of five real-world datasets and significant improvements in predicting ratings in the cold start scenario. The code implementation is available in this https URL.

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:
    - "learning graphs from data a signal representation perspective"
    - "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"
    - "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 "magnn metapath aggregated graph neural network for heterogeneous graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "position aware graph neural networks"
    - "knowledge graph convolutional networks for recommender systems"
    - "representation learning for attributed multiplex heterogeneous network"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "attention is all you need"
    - "heterogeneous information network embedding for recommendation"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"

Connected through hyperedge HE-3 (induced by source paper "diffnet a neural influence and interest diffusion network for social recommendation"):
  with members:
    - "a neural influence diffusion model for social recommendation"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "graph neural networks for social recommendation"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "deepinf social influence prediction with deep learning"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "solving cold start problem in recommendation with attribute graph neural networks"):
  with members:
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "graph neural networks for social recommendation"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "sequential variational autoencoders for collaborative filtering"
    - "neural factorization machines for sparse predictive analytics"
    - "geometric matrix completion with recurrent multi graph neural networks"

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)