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: "point of interest recommendation exploiting self attentive autoencoders with neighbor aware influence"
  Abstract: The rapid growth of Location-based Social Networks (LBSNs) provides a great opportunity to satisfy the strong demand for personalized Point-of-Interest (POI) recommendation services. However, with the tremendous increase of users and POIs, POI recommender systems still face several challenging problems: (1) the hardness of modeling non-linear user-POI interactions from implicit feedback; (2) the difficulty of incorporating context information such as POIs' geographical coordinates. To cope with these challenges, we propose a novel autoencoder-based model to learn the non-linear user-POI relations, namely \textit{SAE-NAD}, which consists of a self-attentive encoder (SAE) and a neighbor-aware decoder (NAD). In particular, unlike previous works equally treat users' checked-in POIs, our self-attentive encoder adaptively differentiates the user preference degrees in multiple aspects, by adopting a multi-dimensional attention mechanism. To incorporate the geographical context information, we propose a neighbor-aware decoder to make users' reachability higher on the similar and nearby neighbors of checked-in POIs, which is achieved by the inner product of POI embeddings together with the radial basis function (RBF) kernel. To evaluate the proposed model, we conduct extensive experiments on three real-world datasets with many state-of-the-art baseline methods and evaluation metrics. The experimental results demonstrate the effectiveness of our model.

Connected through hyperedge HE-1 (induced by source paper "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation"):
  with members:
    - "signed distance based deep memory recommender"
    - "a hybrid approach to music playlist continuation based on playlist song membership"
    - "regularizing matrix factorization with user and item embeddings for recommendation"
    - "collaborative memory network for recommendation systems"
    - "gated attentive autoencoder for content aware recommendation"
    - "adversarial personalized ranking for recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "automatic playlist continuation through a composition of collaborative filters"

Connected through hyperedge HE-2 (induced by source paper "hierarchical gating networks for sequential recommendation"):
  with members:
    - "signed distance based deep memory recommender"
    - "regularizing matrix factorization with user and item embeddings for recommendation"
    - "gated attentive autoencoder for content aware recommendation"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "a simple convolutional generative network for next item recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"

Connected through hyperedge HE-3 (induced by source paper "signed distance based deep memory recommender"):
  with members:
    - "fine grained attention mechanism for neural machine translation"
    - "regularizing matrix factorization with user and item embeddings for recommendation"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "collaborative memory network for recommendation systems"
    - "gated attentive autoencoder for content aware recommendation"
    - "multi pointer co attention networks for recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "a hierarchical self attentive model for recommending user generated item lists"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "pseudo implicit feedback for alleviating data sparsity in top k recommendation"
    - "next item recommendation with self attention"
    - "multi pointer co attention networks for recommendation"
    - "self attentive sequential recommendation"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"
    - "neural collaborative filtering"

Connected through hyperedge HE-5 (induced by source paper "gated attentive autoencoder for content aware recommendation"):
  with members:
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "multi pointer co attention networks for recommendation"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "interacting attention gated recurrent networks for recommendation"

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)