human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "adversarial personalized ranking for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "adversarial training for community question answer selection based on multi scale matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge enhanced attentive learning for answer selection in community question answering systems")
  Member "interpreting deep classifier by visual distillation of dark knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "testing and verification of neural network based safety critical control software a systematic literature review")
  Member "recurrent neural networks with top k gains for session based recommendations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "irgan a minimax game for unifying generative and discriminative information retrieval models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "advances in collaborative filtering and ranking")
  Member "interpretable convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional occupancy networks")
  Member "attentive pooling networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cut based graph learning networks to discover compositional structure of sequential video data")
  Member "a deep relevance matching model for ad hoc retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")

Question: Please tell me which class the source paper 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)