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 training towards robust multimedia recommender system"
  Abstract: With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advance on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. #R##N#In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in this https URL.

Connected through hyperedge HE-1 (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-2 (induced by source paper "mga momentum gradient attack on network"):
  with members:
    - "unsupervised euclidean distance attack on network embedding"
    - "things you may not know about adversarial example a black box adversarial image attack"
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "poisoning attacks to graph based recommender systems"
    - "ga based q attack on community detection"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "curriculum adversarial training"

Connected through hyperedge HE-3 (induced by source paper "graph adversarial training dynamically regularizing based on graph structure"):
  with members:
    - "neural graph collaborative filtering"
    - "adversarial attack on graph structured data"
    - "graph convolutional neural networks for web scale recommender systems"
    - "aaane attention based adversarial autoencoder for multi scale network embedding"
    - "hierarchical graph representation learning with differentiable pooling"
    - "adversarial personalized ranking for recommendation"
    - "deep learning on graphs a survey"
    - "large scale learnable graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "interest related item similarity model based on multimodal data for top n recommendation"):
  with members:
    - "achieving efficient and secure data acquisition for cloud supported internet of things in smart grid"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "aesthetic based clothing recommendation"
    - "nais neural attentive item similarity model for recommendation"
    - "a multimodal recommender system for large scale assortment generation in e commerce"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-5 (induced by source paper "generating reliable friends via adversarial training to improve social recommendation"):
  with members:
    - "a neural influence diffusion model for social recommendation"
    - "graph neural networks for social recommendation"
    - "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)