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: "collaborative filtering with social exposure a modular approach to social recommendation"
  Abstract: This paper is concerned with how to make efficient use of social information to improve recommendations. Most existing social recommender systems assume people share similar preferences with their social friends. Which, however, may not hold true due to various motivations of making online friends and dynamics of online social networks. Inspired by recent causal process based recommendations that first model user exposures towards items and then use these exposures to guide rating prediction, we utilize social information to capture user exposures rather than user preferences. We assume that people get information of products from their online friends and they do not have to share similar preferences, which is less restrictive and seems closer to reality. Under this new assumption, in this paper, we present a novel recommendation approach (named SERec) to integrate social exposure into collaborative filtering. We propose two methods to implement SERec, namely social regularization and social boosting, each with different ways to construct social exposures. Experiments on four real-world datasets demonstrate that our methods outperform the state-of-the-art methods on top-N recommendations. Further study compares the robustness and scalability of the two proposed methods.

Connected through hyperedge HE-1 (induced by source paper "fast adaptively weighted matrix factorization for recommendation with implicit feedback"):
  with members:
    - "adversarial sampling and training for semi supervised information retrieval"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "on sampling strategies for neural network based collaborative filtering"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "a generic coordinate descent framework for learning from implicit feedback"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-2 (induced by source paper "tripartite heterogeneous graph propagation for large scale social recommendation"):
  with members:
    - "graph neural networks for social recommendation"
    - "representation learning for attributed multiplex heterogeneous network"
    - "graph convolutional neural networks for web scale recommender systems"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "pitfalls of graph neural network evaluation"
    - "applying deep learning to airbnb search"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-3 (induced by source paper "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "socialgcn an efficient graph convolutional network based model for social recommendation"
    - "multiresolution graph attention networks for relevance matching"
    - "deepinf social influence prediction with deep learning"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "user sensitive recommendation ensemble with clustered multi task learning"):
  with members:
    - "feature weighted linear stacking"

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)