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 mahalanobis distance based attentive song recommender for automatic playlist continuation"
  Abstract: In this paper, we aim to solve the automatic playlist continuation (APC) problem by modeling complex interactions among users, playlists, and songs using only their interaction data. Prior methods mainly rely on dot product to account for similarities, which is not ideal as dot product is not metric learning, so it does not convey the important inequality property. Based on this observation, we propose three novel deep learning approaches that utilize Mahalanobis distance. Our first approach uses user-playlist-song interactions, and combines Mahalanobis distance scores between (i) a target user and a target song, and (ii) between a target playlist and the target song to account for both the user's preference and the playlist's theme. Our second approach measures song-song similarities by considering Mahalanobis distance scores between the target song and each member song (i.e., existing song) in the target playlist. The contribution of each distance score is measured by our proposed memory metric-based attention mechanism. In the third approach, we fuse the two previous models into a unified model to further enhance their performance. In addition, we adopt and customize Adversarial Personalized Ranking (APR) for our three approaches to further improve their robustness and predictive capabilities. Through extensive experiments, we show that our proposed models outperform eight state-of-the-art models in two large-scale real-world datasets.

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"
    - "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"
    - "adversarial training towards robust multimedia recommender system"

Connected through hyperedge HE-2 (induced by source paper "hyperml a boosting metric learning approach in hyperbolic space for recommender systems"):
  with members:
    - "scalable hyperbolic recommender systems"
    - "signed distance based deep memory recommender"
    - "hyperbolic attention networks"
    - "neurec on nonlinear transformation for personalized ranking"
    - "riemannian adaptive optimization methods"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "outer product based neural collaborative filtering"
    - "hyperbolic representation learning for fast and efficient neural question answering"

Connected through hyperedge HE-3 (induced by source paper "consistency aware recommendation for user generated itemlist continuation"):
  with members:
    - "a hierarchical self attentive model for recommending user generated item lists"
    - "an analysis of approaches taken in the acm recsys challenge 2018 for automatic music playlist continuation"
    - "collaborative memory network for recommendation systems"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "nais neural attentive item similarity model for recommendation"
    - "self attentive sequential recommendation"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"

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)