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: "a visual and textual recurrent neural network for sequential prediction"
  Abstract: Sequential prediction is a fundamental task for Web applications. Due to the insufficiency of user feedbacks, sequential prediction usually suffers from the cold start problem. There are two kinds of popular approaches based on matrix factorization (MF) and Markov chains (MC) for item prediction. MF methods factorize the user-item matrix to learn general tastes of users. MC methods predict the next behavior based on recent behaviors. However, they have limitations. MF methods can merge additional information to address cold start but could not capture dynamic properties of user's interest, and MC based sequential methods have difficulty in addressing cold start and has a strong Markov assumption that the next state only depends on the last state. In this work, to deal with the cold start problem of sequential prediction, we propose a RNN model adopting visual and textual content of items, which is named as $\mathbf{V}$isual and $\mathbf{T}$extual $\mathbf{R}$ecurrent $\mathbf{N}$eural $\mathbf{N}$etwork ($\mathbf{VT}$-$\mathbf{RNN}$). We can simultaneously learn the sequential latent vectors that dynamically capture the user's interest, as well as content-based representations that contribute to address the cold start. Experiments on two real-world datasets show that our proposed VT-RNN model can effectively generate the personalized ranking list and significantly alleviate the cold start problem.

Connected through hyperedge HE-1 (induced by source paper "interest related item similarity model based on multimodal data for top n recommendation"):
  with members:
    - "adversarial training towards robust multimedia recommender system"
    - "achieving efficient and secure data acquisition for cloud supported internet of things in smart grid"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "aesthetic based clothing recommendation"
    - "nais neural attentive item similarity model for recommendation"
    - "a multimodal recommender system for large scale assortment generation in e commerce"
    - "attention is all you need"
    - "neural collaborative filtering"

Connected through hyperedge HE-2 (induced by source paper "aesthetic based clothing recommendation"):
  with members:
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "birank towards ranking on bipartite graphs"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"
    - "brain inspired deep networks for image aesthetics assessment"
    - "describing human aesthetic perception by deeply learned attributes from flickr"

Connected through hyperedge HE-3 (induced by source paper "visually explainable recommendation"):
  with members:
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "neural collaborative filtering"
    - "neural rating regression with abstractive tips generation for recommendation"
    - "explainable recommendation theory and applications"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "interpretable recommendations via overlapping co clusters"
    - "explaining reviews and ratings with paco poisson additive co clustering"
    - "vbpr visual bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "a hierarchical contextual attention based gru network for sequential recommendation"):
  with members:
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "context aware sequential recommendation"
    - "multi behavioral sequential prediction with recurrent log bilinear model"
    - "mv rnn a multi view recurrent neural network for sequential recommendation"
    - "crafting gbd net for object detection"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "effective approaches to attention based neural machine translation"
    - "reasoning about entailment with neural attention"

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)