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: "recurrent poisson factorization for temporal recommendation"
  Abstract: Poisson factorization is a probabilistic model of users and items for recommendation systems, where the so-called implicit consumer data is modeled by a factorized Poisson distribution. There are many variants of Poisson factorization methods who show state-of-the-art performance on real-world recommendation tasks. However, most of them do not explicitly take into account the temporal behavior and the recurrent activities of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model and brings to the table a rich family of time-sensitive factorization models. To elaborate, we instantiate several variants of RPF who are capable of handling dynamic user preferences and item specification (DRPF), modeling the social-aspect of product adoption (SRPF), and capturing the consumption heterogeneity among users and items (HRPF). We also develop a variational algorithm for approximate posterior inference that scales up to massive data sets. Furthermore, we demonstrate RPF's superior performance over many state-of-the-art methods on synthetic dataset, and large scale real-world datasets on music streaming logs, and user-item interactions in M-Commerce platforms.

Connected through hyperedge HE-1 (induced by source paper "a collaborative filtering model with heterogeneous neural networks for recommender systems"):
  with members:
    - "collaborative filtering with topic and social latent factors incorporating implicit feedback"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "hybrid recommender system based on autoencoders"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "coupled variational recurrent collaborative filtering"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "sequential variational autoencoders for collaborative filtering"
    - "a hybrid variational autoencoder for collaborative filtering"
    - "neural collaborative filtering"
    - "discrete event continuous time rnns"
    - "collaborative recurrent autoencoder recommend while learning to fill in the blanks"
    - "streaming recommender systems"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "choracle a unified statistical framework for churn prediction"):
  with members:
    - "wasserstein learning of deep generative point process models"
    - "tensorflow a system for large scale machine learning"
    - "correlated cascades compete or cooperate"
    - "behavioral modeling for churn prediction early indicators and accurate predictors of custom defection and loyalty"
    - "a recurrent latent variable model for sequential data"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "joint modeling of event sequence and time series with attentional twin recurrent neural networks"):
  with members:
    - "distilling information reliability and source trustworthiness from digital traces"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "multistage campaigning in social networks"
    - "learning granger causality for hawkes processes"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "show attend and tell neural image caption generation with visual attention"
    - "equilibrated adaptive learning rates for non convex optimization"

Connected through hyperedge HE-5 (induced by source paper "interacting attention gated recurrent networks for recommendation"):
  with members:
    - "neural collaborative filtering"
    - "sherlock sparse hierarchical embeddings for visually aware one class collaborative filtering"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "temporal attention gated model for robust sequence classification"
    - "item2vec neural item embedding for collaborative filtering"
    - "session based recommendations with recurrent neural networks"
    - "image based recommendations on styles and substitutes"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-6 (induced by source paper "fake news mitigation via point process based intervention"):
  with members:
    - "distilling information reliability and source trustworthiness from digital traces"
    - "wasserstein learning of deep generative point process models"
    - "multistage campaigning in social networks"
    - "steering opinion dynamics in information diffusion networks"
    - "detecting weak changes in dynamic events over networks"
    - "learning granger causality for hawkes processes"
    - "smart broadcasting do you want to be seen"
    - "correlated cascades compete or cooperate"

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.SI(Social and Information Networks)