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: "deepinf social influence prediction with deep learning"
  Abstract: Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them. Consequently, an effective social influence prediction for each user is critical for a variety of applications such as online recommendation and advertising.   Conventional social influence prediction approaches typically design various hand-crafted rules to extract user- and network-specific features. However, their effectiveness heavily relies on the knowledge of domain experts. As a result, it is usually difficult to generalize them into different domains. Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence. In general, DeepInf takes a user's local network as the input to a graph neural network for learning her latent social representation. We design strategies to incorporate both network structures and user-specific features into convolutional neural and attention networks. Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly outperforms traditional feature engineering-based approaches, suggesting the effectiveness of representation learning for social applications.

Connected through hyperedge HE-1 (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"
    - "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"
    - "learning structural node embeddings via diffusion wavelets"

Connected through hyperedge HE-2 (induced by source paper "lightgcn simplifying and powering graph convolution network for recommendation"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "kgat knowledge graph attention network for recommendation"
    - "a neural influence diffusion model for social recommendation"
    - "collaborative similarity embedding for recommender systems"
    - "λopt learn to regularize recommender models in finer levels"
    - "neural graph collaborative filtering"
    - "knowledge graph convolutional networks for recommender systems"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-3 (induced by source paper "deep collaborative embedding for information cascade prediction"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "iad interaction aware diffusion framework in social networks"
    - "attributed social network embedding"
    - "collaborative inference of coexisting information diffusions"
    - "a survey on network embedding"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "topological recurrent neural network for diffusion prediction"
    - "bl mne emerging heterogeneous social network embedding through broad learning with aligned autoencoder"

Connected through hyperedge HE-4 (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"
    - "star gcn stacked and reconstructed graph convolutional networks for 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"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction"):
  with members:
    - "an induced multi relational framework for answer selection in community question answer platforms"
    - "dynamic graph representation learning via self attention networks"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "motif based convolutional neural network on graphs"
    - "topological recurrent neural network for diffusion prediction"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "the mass fake news and cognition security"):
  with members:
    - "the role of user profile for fake news detection"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "studying fake news via network analysis detection and mitigation"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "detection and resolution of rumours in social media a survey"

Connected through hyperedge HE-7 (induced by source paper "a logic based framework leveraging neural networks for studying the evolution of neurological disorders"):
  with members:
    - "the seventh answer set programming competition design and results"
    - "the external interface for extending wasp"
    - "asp core 2 input language format"
    - "a comprehensive survey on graph neural networks"
    - "interpreting cnns via decision trees"
    - "constraint answer set programming without grounding"
    - "automated verification of neural networks advances challenges and perspectives"
    - "visual interpretability for deep learning a survey"

Connected through hyperedge HE-8 (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"

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.SI(Social and Information Networks)