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: "hierarchical attention networks"
  Abstract: We propose a novel attention network, which accurately attends to target objects of various scales and shapes in images through multiple stages. The proposed network enables multiple layers to estimate attention in a convolutional neural network (CNN). The hierarchical attention model gradually suppresses irrelevant regions in an input image using a progressive attentive process over multiple CNN layers. The attentive process in each layer determines whether to pass or suppress feature maps for use in the next convolution. We employ local contexts to estimate attention probability at each location since it is difficult to infer accurate attention by observing a feature vector from a single location only. The experiments on synthetic and real datasets show that the proposed attention network outperforms traditional attention methods in various attribute prediction tasks.

Connected through hyperedge HE-1 (induced by source paper "text classification algorithms a survey"):
  with members:
    - "rmdl random multimodel deep learning for classification"
    - "analysis of railway accidents narratives using deep learning"
    - "one shot and few shot learning of word embeddings"
    - "deep contextualized word representations"
    - "an improvement of data classification using random multimodel deep learning rmdl"
    - "gakco a fast gapped k mer string kernel using counting"
    - "learning important features through propagating activation differences"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-2 (induced by source paper "res3atn deep 3d residual attention network for hand gesture recognition in videos"):
  with members:
    - "real time hand gesture detection and classification using convolutional neural networks"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "automatic brain tumor detection and segmentation using u net based fully convolutional networks"
    - "residual attention network for image classification"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-3 (induced by source paper "signed distance based deep memory recommender"):
  with members:
    - "fine grained attention mechanism for neural machine translation"
    - "regularizing matrix factorization with user and item embeddings for recommendation"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "collaborative memory network for recommendation systems"
    - "gated attentive autoencoder for content aware recommendation"
    - "point of interest recommendation exploiting self attentive autoencoders with neighbor aware influence"
    - "multi pointer co attention networks for recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"

Connected through hyperedge HE-4 (induced by source paper "conditionally learn to pay attention for sequential visual task"):
  with members:
    - "learn to pay attention"
    - "a unified query based generative model for question generation and question answering"
    - "diversity driven attention model for query based abstractive summarization"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "image captioning with semantic attention"
    - "progressive attention networks for visual attribute prediction"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "id aware quality for set based person re identification"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "multi scale 3d convolution network for video based person re identification"
    - "sta spatial temporal attention for large scale video based person re identification"
    - "learn to pay attention"
    - "spatial and temporal mutual promotion for video based person re identification"
    - "video based person re identification with accumulative motion context"
    - "person re identification via recurrent feature aggregation"
    - "re ranking person re identification with k reciprocal encoding"

Connected through hyperedge HE-6 (induced by source paper "multiple instance dense connected convolution neural network for aerial image scene classification"):
  with members:
    - "attention based deep multiple instance learning"
    - "dota a large scale dataset for object detection in aerial images"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "remote sensing image scene classification benchmark and state of the art"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "progressive attention networks for visual attribute prediction"
    - "densely connected convolutional networks"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-7 (induced by source paper "attention based convolutional neural network for weakly labeled human activities recognition with wearable sensors"):
  with members:
    - "human activity recognition based on wearable sensor data a standardization of the state of the art"
    - "learn to pay attention"
    - "recurrent models of visual attention"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "targeted kernel networks faster convolutions with attentive regularization"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "non local neural networks"
    - "dynamic routing between capsules"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"
    - "interpretable convolutional neural networks"
    - "attentional pooling for action recognition"
    - "more is less a more complicated network with less inference complexity"
    - "attentive recurrent comparators"

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)