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: "survey on the attention based rnn model and its applications in computer vision"
  Abstract: The recurrent neural networks (RNN) can be used to solve the sequence to sequence problem, where both the input and the output have sequential structures. Usually there are some implicit relations between the structures. However, it is hard for the common RNN model to fully explore the relations between the sequences. In this survey, we introduce some attention based RNN models which can focus on different parts of the input for each output item, in order to explore and take advantage of the implicit relations between the input and the output items. The different attention mechanisms are described in detail. We then introduce some applications in computer vision which apply the attention based RNN models. The superiority of the attention based RNN model is shown by the experimental results. At last some future research directions are given.

Connected through hyperedge HE-1 (induced by source paper "an attentive survey of attention models"):
  with members:
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "targeted sentiment classification with attentional encoder network"
    - "attention please a critical review of neural attention models in natural language processing"
    - "attention models in graphs a survey"
    - "dynamic meta embeddings for improved sentence representations"
    - "self attentive sequential recommendation"
    - "self attention a better building block for sentiment analysis neural network classifiers"
    - "atrank an attention based user behavior modeling framework for recommendation"

Connected through hyperedge HE-2 (induced by source paper "yotube searching action proposal via recurrent and static regression networks"):
  with members:
    - "rgbd salient object detection via deep fusion"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "deep learning for video classification and captioning"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "searching action proposals via spatial actionness estimation and temporal path inference and tracking"
    - "object detection from video tubelets with convolutional neural networks"
    - "ssd single shot multibox detector"
    - "deep learning for detecting multiple space time action tubes in videos"

Connected through hyperedge HE-3 (induced by source paper "recurrent attention unit"):
  with members:
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "quasi recurrent neural networks"
    - "recurrent memory network for language modeling"
    - "effective approaches to attention based neural machine translation"
    - "skip thought vectors"
    - "show attend and tell neural image caption generation with visual attention"
    - "grid long short term memory"

Connected through hyperedge HE-4 (induced by source paper "mindid person identification from brain waves through attention based recurrent neural network"):
  with members:
    - "multi modality sensor data classification with selective attention"
    - "interpretable parallel recurrent neural networks with convolutional attentions for multi modality activity modeling"
    - "multi person brain activity recognition via comprehensive eeg signal analysis"
    - "xgboost a scalable tree boosting system"
    - "effective approaches to attention based neural machine translation"
    - "end to end attention based large vocabulary speech recognition"
    - "adam a method for stochastic optimization"
    - "multiple object recognition with visual attention"

Connected through hyperedge HE-5 (induced by source paper "learning sentence representation with guidance of human attention"):
  with members:
    - "siamese cbow optimizing word embeddings for sentence representations"
    - "modeling human reading with neural attention"
    - "learning distributed representations of sentences from unlabelled data"
    - "dynamic memory networks for visual and textual question answering"
    - "improving sentence compression by learning to predict gaze"
    - "exploring the limits of language modeling"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "skip thought vectors"

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.CV(Computer Vision and Pattern Recognition)