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: "a neural influence diffusion model for social recommendation"
  Abstract: Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering(CF) provides a way to learn user and item embeddings from the user-item interaction history. However, the performance is limited due to the sparseness of user behavior data. With the emergence of online social networks, social recommender systems have been proposed to utilize each user's local neighbors' preferences to alleviate the data sparsity for better user embedding modeling. We argue that, for each user of a social platform, her potential embedding is influenced by her trusted users. As social influence recursively propagates and diffuses in the social network, each user's interests change in the recursive process. Nevertheless, the current social recommendation models simply developed static models by leveraging the local neighbors of each user without simulating the recursive diffusion in the global social network, leading to suboptimal recommendation performance. In this paper, we propose a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation. For each user, the diffusion process starts with an initial embedding that fuses the related features and a free user latent vector that captures the latent behavior preference. The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues. We further show that our proposed model is general and could be applied when the user~(item) attributes or the social network structure is not available. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model, with more than 13% performance improvements over the best baselines.

Connected through hyperedge HE-1 (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"
    - "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"
    - "predict then propagate graph neural networks meet personalized pagerank"

Connected through hyperedge HE-2 (induced by source paper "diffnet a neural influence and interest diffusion network for social recommendation"):
  with members:
    - "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"
    - "deepinf social influence prediction with deep learning"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "revisiting graph based collaborative filtering a linear residual graph convolutional network approach"):
  with members:
    - "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"
    - "spectral collaborative filtering"
    - "neural collaborative filtering"
    - "geometric matrix completion with recurrent multi graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "generating reliable friends via adversarial training to improve social recommendation"):
  with members:
    - "graph neural networks for social recommendation"
    - "adversarial training towards robust multimedia recommender system"
    - "adversarial personalized ranking for recommendation"
    - "neural collaborative filtering"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "extracting implicit social relation for social recommendation techniques in user rating prediction"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

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)