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: "mv rnn a multi view recurrent neural network for sequential recommendation"
  Abstract: Sequential recommendation is a fundamental task for network applications, and it usually suffers from the item cold start problem due to the insufficiency of user feedbacks. There are currently three kinds of popular approaches which are respectively based on matrix factorization (MF) of collaborative filtering, Markov chain (MC), and recurrent neural network (RNN). Although widely used, they have some limitations. MF based methods could not capture dynamic user's interest. The strong Markov assumption greatly limits the performance of MC based methods. RNN based methods are still in the early stage of incorporating additional information. Based on these basic models, many methods with additional information only validate incorporating one modality in a separate way. In this work, to make the sequential recommendation and deal with the item cold start problem, we propose a Multi-View Recurrent Neural Network (MV-RNN}) model. Given the latent feature, MV-RNN can alleviate the item cold start problem by incorporating visual and textual information. First, At the input of MV-RNN, three different combinations of multi-view features are studied, like concatenation, fusion by addition and fusion by reconstructing the original multi-modal data. MV-RNN applies the recurrent structure to dynamically capture the user's interest. Second, we design a separate structure and a united structure on the hidden state of MV-RNN to explore a more effective way to handle multi-view features. Experiments on two real-world datasets show that MV-RNN can effectively generate the personalized ranking list, tackle the missing modalities problem and significantly alleviate the item cold start problem.

Connected through hyperedge HE-1 (induced by source paper "dressing as a whole outfit compatibility learning based on node wise graph neural networks"):
  with members:
    - "session based recommendation with graph neural network"
    - "constructing narrative event evolutionary graph for script event prediction"
    - "situation recognition with graph neural networks"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "compatibility family learning for item recommendation and generation"
    - "style2vec representation learning for fashion items from style sets"
    - "learning fashion compatibility with bidirectional lstms"

Connected through hyperedge HE-2 (induced by source paper "nairs a neural attentive interpretable recommendation system"):
  with members:
    - "nais neural attentive item similarity model for recommendation"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "reinforced mnemonic reader for machine comprehension"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "session based recommendations with recurrent neural networks"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (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"
    - "a visual and textual recurrent neural network for sequential prediction"
    - "context aware sequential recommendation"
    - "multi behavioral sequential prediction with recurrent log bilinear model"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "leveraging long and short term information in content aware movie recommendation"):
  with members:
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "generative adversarial text to image synthesis"
    - "collaborative filtering with recurrent neural networks"
    - "session based recommendations with recurrent neural networks"
    - "conditional generative adversarial nets"

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)