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: "spectral collaborative filtering"
  Abstract: Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the cold-start problem, which has a significantly negative impact on users' experiences with Recommender Systems (RS). In this paper, to overcome the aforementioned drawback, we first formulate the relationships between users and items as a bipartite graph. Then, we propose a new spectral convolution operation directly performing in the spectral domain, where not only the proximity information of a graph but also the connectivity information hidden in the graph are revealed. With the proposed spectral convolution operation, we build a deep recommendation model called Spectral Collaborative Filtering (SpectralCF). Benefiting from the rich information of connectivity existing in the spectral domain, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the cold-start problem for CF. To the best of our knowledge, SpectralCF is the first CF-based method directly learning from the spectral domains of user-item bipartite graphs. We apply our method on several standard datasets. It is shown that SpectralCF significantly out-performs state-of-the-art models. Code and data are available at https://github.com/lzheng21/SpectralCF.

Connected through hyperedge HE-1 (induced by source paper "revisiting graph based collaborative filtering a linear residual graph convolutional network approach"):
  with members:
    - "a neural influence diffusion model for social recommendation"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "simplifying graph convolutional networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "neural collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "basconv aggregating heterogeneous interactions for basket recommendation with graph convolutional neural network"):
  with members:
    - "modeling complementary products and customer preferences with context knowledge for online recommendation"
    - "personalized bundle list recommendation"
    - "jscn joint spectral convolutional network for cross domain recommendation"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "complete the look scene based complementary product recommendation"
    - "neural collaborative filtering"
    - "item2vec neural item embedding for collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "neural graph collaborative filtering"):
  with members:
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "graph convolutional neural networks for web scale recommender systems"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "collaborative memory network for recommendation systems"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-4 (induced by source paper "inductive matrix completion based on graph neural networks"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "link prediction based on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "how powerful are graph neural networks"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-5 (induced by source paper "jscn joint spectral convolutional network for cross domain recommendation"):
  with members:
    - "deep cross networks with aesthetic preference for cross domain recommendation"
    - "deep learning based recommender system a survey and new perspectives"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "conet collaborative cross networks for cross domain recommendation"
    - "neural collaborative filtering"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "cross stitch networks for multi task learning"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"

Connected through hyperedge HE-6 (induced by source paper "a neural influence diffusion model for social recommendation"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "deepinf social influence prediction with deep learning"
    - "a hierarchical attention model for social contextual image recommendation"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "representation learning on graphs methods and applications"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-7 (induced by source paper "are we really making much progress a worrying analysis of recent neural recommendation approaches"):
  with members:
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "attentive neural architecture incorporating song features for music recommendation"
    - "collaborative memory network for recommendation systems"
    - "multi pointer co attention networks for recommendation"
    - "deep reinforcement learning that matters"
    - "neural collaborative filtering"

Connected through hyperedge HE-8 (induced by source paper "a troubling analysis of reproducibility and progress in recommender systems research"):
  with members:
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "embarrassingly shallow autoencoders for sparse data"
    - "on the difficulty of evaluating baselines a study on recommender systems"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "attentive neural architecture incorporating song features for music recommendation"
    - "outer product based neural collaborative filtering"
    - "collaborative memory network for recommendation systems"
    - "meta prod2vec product embeddings using side information for recommendation"

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)