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: "adversarial sampling and training for semi supervised information retrieval"
  Abstract: Modern ad-hoc retrieval models learned with implicit feedback have two problems in general. First, there are usually much more non-clicked documents than clicked documents, and many of the non-clicked documents are not informational. Second, modern ad-hoc retrieval models are vulnerable to adversarial examples due to the linear nature in the models. To solve the problems at the same time, we propose adversarial training methods that can overcome those weaknesses. Our key idea is to combine adversarial training with adversarial sampling in order to obtain very difficult examples, which are informational and can attack the linear nature of the models. Specifically, we adversarially sample difficult training examples, and based on them, we further generate adversarial examples that are even more difficult. To make the models robust, the generated adversarial examples as well as the original training examples are then given to the models for joint optimization. Experiments are performed on benchmark data sets for common ad-hoc retrieval tasks such as Web search, item recommendation, and question answering. The proposed methods are closely compared with IRGAN, which is a recent relevant approach that employs adversarial training. Experiment results indicate that the proposed methods significantly outperform strong baselines especially for high-ranked documents, and they outperform IRGAN in NDCG@5 using only 5% of labeled data for the Web search task.

Connected through hyperedge HE-1 (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"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable reasoning over knowledge graphs for recommendation"

Connected through hyperedge HE-2 (induced by source paper "fast adaptively weighted matrix factorization for recommendation with implicit feedback"):
  with members:
    - "collaborative filtering with social exposure a modular approach to social recommendation"
    - "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-3 (induced by source paper "regularized adversarial sampling and deep time aware attention for click through rate prediction"):
  with members:
    - "adversarial contrastive estimation"
    - "telepath understanding users from a human vision perspective in large scale recommender systems"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "attention is all you need"
    - "deep cross network for ad click predictions"
    - "optimizing gross merchandise volume via dnn mab dynamic ranking paradigm"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"

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)