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: "neurec on nonlinear transformation for personalized ranking"
  Abstract: Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establishes an integrated network to combine non-linear transformation with latent factors. We further design two variants of NeuRec: user-based NeuRec and item-based NeuRec, by concentrating on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.

Connected through hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  with members:
    - "session based recommendation with graph neural network"
    - "contextual hybrid session based news recommendation with recurrent neural networks"
    - "on the importance of news content representation in hybrid neural session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "multi pointer co attention networks for recommendation"
    - "neural collaborative ranking"

Connected through hyperedge HE-2 (induced by source paper "collaborative filtering with a synthetic feedback loop"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "improving textual network learning with variational homophilic embeddings"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "wasserstein autoencoders for collaborative filtering"
    - "outer product based neural collaborative filtering"
    - "top k off policy correction for a reinforce recommender system"
    - "zero shot learning via class conditioned deep generative models"
    - "neural collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "multi scale quasi rnn for next item recommendation"):
  with members:
    - "session based recommendation with graph neural network"
    - "deep learning based recommender system a survey and new perspectives"
    - "sequence aware recommender systems"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "multi pointer co attention networks for recommendation"
    - "a simple convolutional generative network for next item recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"

Connected through hyperedge HE-4 (induced by source paper "hyperml a boosting metric learning approach in hyperbolic space for recommender systems"):
  with members:
    - "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation"
    - "scalable hyperbolic recommender systems"
    - "signed distance based deep memory recommender"
    - "hyperbolic attention networks"
    - "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-5 (induced by source paper "quaternion collaborative filtering for recommendation"):
  with members:
    - "deeprec an open source toolkit for deep learning based recommendation"
    - "deep learning based recommender system a survey and new perspectives"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "next item recommendation with self attention"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "autosvd an efficient hybrid collaborative filtering model via contractive auto encoders"

Connected through hyperedge HE-6 (induced by source paper "deeprec an open source toolkit for deep learning based recommendation"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "next item recommendation with self attention"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "neural factorization machines for sparse predictive analytics"
    - "neural rating regression with abstractive tips generation for recommendation"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "wide deep learning for recommender systems"

Connected through hyperedge HE-7 (induced by source paper "self attentive neural collaborative filtering"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "outer product based neural collaborative filtering"
    - "reasoning with sarcasm by reading in between"
    - "multi pointer co attention networks for recommendation"
    - "couplenet paying attention to couples with coupled attention for relationship recommendation"
    - "neural brane neural bayesian personalized ranking for attributed network embedding"
    - "personalized top n sequential recommendation via convolutional sequence embedding"

Connected through hyperedge HE-8 (induced by source paper "multi pointer co attention networks for recommendation"):
  with members:
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "neural factorization machines for sparse predictive analytics"
    - "attention is all you need"
    - "a compare propagate architecture with alignment factorization for natural language inference"
    - "neural collaborative filtering"
    - "neural rating regression with abstractive tips generation for recommendation"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "joint deep modeling of users and items using reviews 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)