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: "vista a visually socially and temporally aware model for artistic recommendation"
  Abstract: Understanding users' interactions with highly subjective content---like artistic images---is challenging due to the complex semantics that guide our preferences. On the one hand one has to overcome `standard' recommender systems challenges, such as dealing with large, sparse, and long-tailed datasets. On the other, several new challenges present themselves, such as the need to model content in terms of its visual appearance, or even social dynamics, such as a preference toward a particular artist that is independent of the art they create. In this paper we build large-scale recommender systems to model the dynamics of a vibrant digital art community, Behance, consisting of tens of millions of interactions (clicks and 'appreciates') of users toward digital art. Methodologically, our main contributions are to model (a) rich content, especially in terms of its visual appearance; (b) temporal dynamics, in terms of how users prefer 'visually consistent' content within and across sessions; and (c) social dynamics, in terms of how users exhibit preferences both towards certain art styles, as well as the artists themselves.

Connected through hyperedge HE-1 (induced by source paper "sequential recommendation with dual side neighbor based collaborative relation modeling"):
  with members:
    - "lifelong sequential modeling with personalized memorization for user response prediction"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "session based social recommendation via dynamic graph attention networks"
    - "deep learning based recommender system a survey and new perspectives"
    - "outer product based neural collaborative filtering"
    - "repeatnet a repeat aware neural recommendation machine for session based recommendation"
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "recurrent neural networks for long and short term sequential recommendation"

Connected through hyperedge HE-2 (induced by source paper "deep learning for sequential recommendation algorithms influential factors and evaluations"):
  with members:
    - "session based recommendation with graph neural network"
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "learning to recommend with multiple cascading behaviors"
    - "research commentary on recommendations with side information a survey and research directions"
    - "improving end to end sequential recommendations with intent aware diversification"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "a survey on session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "lifelong sequential modeling with personalized memorization for user response prediction"):
  with members:
    - "on training recurrent neural networks for lifelong learning"
    - "collaborative memory network for recommendation systems"
    - "learning multi touch conversion attribution with dual attention mechanisms for online advertising"
    - "next item recommendation with self attention"
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "recurrent neural networks for long and short term sequential recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"

Connected through hyperedge HE-4 (induced by source paper "distilling structured knowledge into embeddings for explainable and accurate recommendation"):
  with members:
    - "neural ir meets graph embedding a ranking model for product search"
    - "jointly learning explainable rules for recommendation with knowledge graph"
    - "deep learning based recommender system a survey and new perspectives"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable recommendation a survey and new perspectives"
    - "neural factorization machines for sparse predictive analytics"
    - "deep mutual learning"
    - "neural collaborative filtering"

Connected through hyperedge HE-5 (induced by source paper "mmalfm explainable recommendation by leveraging reviews and images"):
  with members:
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "transnets learning to transform for recommendation"
    - "sherlock sparse hierarchical embeddings for visually aware one class collaborative filtering"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"

Connected through hyperedge HE-6 (induced by source paper "a dynamic embedding model of the media landscape"):
  with members:
    - "selection bias in news coverage learning it fighting it"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "two tales of the world comparison of widely used world news datasets gdelt and eventregistry"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-7 (induced by source paper "self attentive sequential recommendation"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "translation based recommendation"
    - "visually aware fashion recommendation and design with generative image models"

Connected through hyperedge HE-8 (induced by source paper "a hierarchical attention model for social contextual image recommendation"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "nais neural attentive item similarity model for recommendation"
    - "neural collaborative filtering"
    - "a survey on network embedding"
    - "related pins at pinterest the evolution of a real world recommender system"
    - "a context aware attention network for interactive question answering"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "autosvd an efficient hybrid collaborative filtering model via contractive auto encoders"

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)