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: "on sampling strategies for neural network based collaborative filtering"
  Abstract: Recent advances in neural networks have inspired people to design hybrid recommendation algorithms that can incorporate both (1) user-item interaction information and (2) content information including image, audio, and text. Despite their promising results, neural network-based recommendation algorithms pose extensive computational costs, making it challenging to scale and improve upon. In this paper, we propose a general neural network-based recommendation framework, which subsumes several existing state-of-the-art recommendation algorithms, and address the efficiency issue by investigating sampling strategies in the stochastic gradient descent training for the framework. We tackle this issue by first establishing a connection between the loss functions and the user-item interaction bipartite graph, where the loss function terms are defined on links while major computation burdens are located at nodes. We call this type of loss functions "graph-based" loss functions, for which varied mini-batch sampling strategies can have different computational costs. Based on the insight, three novel sampling strategies are proposed, which can significantly improve the training efficiency of the proposed framework (up to $\times 30$ times speedup in our experiments), as well as improving the recommendation performance. Theoretical analysis is also provided for both the computational cost and the convergence. We believe the study of sampling strategies have further implications on general graph-based loss functions, and would also enable more research under the neural network-based recommendation framework.

Connected through hyperedge HE-1 (induced by source paper "a simple framework for contrastive learning of visual representations"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "on mutual information maximization for representation learning"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "unsupervised data augmentation for consistency training"

Connected through hyperedge HE-2 (induced by source paper "recommender systems based on generative adversarial networks a problem driven perspective"):
  with members:
    - "deep adversarial social recommendation"
    - "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation"
    - "kgat knowledge graph attention network for recommendation"
    - "deep learning to address candidate generation and cold start challenges in recommender systems a research survey"
    - "generating reliable friends via adversarial training to improve social recommendation"
    - "cold start playlist recommendation with multitask learning"
    - "exs explainable search using local model agnostic interpretability"
    - "adversarial collaborative auto encoder for top n recommendation"

Connected through hyperedge HE-3 (induced by source paper "reinforced negative sampling over knowledge graph for recommendation"):
  with members:
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "knowledge aware graph neural networks with label smoothness regularization for recommender systems"
    - "neural graph collaborative filtering"
    - "adversarial sampling and training for semi supervised information retrieval"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"

Connected through hyperedge HE-4 (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"
    - "collaborative filtering with social exposure a modular approach to social recommendation"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "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-5 (induced by source paper "leveraging trust and distrust in recommender systems via deep learning"):
  with members:
    - "neural collaborative filtering"
    - "node2vec scalable feature learning for networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "collaborative deep learning for recommender systems"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "sitting closer to friends than enemies revisited"

Connected through hyperedge HE-6 (induced by source paper "a neural attention model for adaptive learning of social friends preferences"):
  with members:
    - "collaborative memory network for recommendation systems"
    - "multi pointer co attention networks for recommendation"
    - "neural collaborative filtering"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-7 (induced by source paper "attribute aware collaborative filtering survey and classification"):
  with members:
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "collaborative recurrent autoencoder recommend while learning to fill in the blanks"
    - "multi view machines"
    - "vbpr visual bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-8 (induced by source paper "deeply supervised semantic model for click through rate prediction in sponsored search"):
  with members:
    - "match tensor a deep relevance model for search"
    - "very deep convolutional networks for text classification"
    - "augmenting supervised neural networks with unsupervised objectives for large scale image classification"
    - "going deeper with convolutions"
    - "sequential click prediction for sponsored search with recurrent neural networks"
    - "neural machine translation by jointly learning to align and translate"
    - "a theoretical analysis of ndcg type ranking measures"
    - "efficient estimation of word representations in vector space"

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.LG(Machine Learning)