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: "two stream lstm a deep fusion framework for human action recognition"
  Abstract: In this paper we address the problem of human action recognition from video sequences. Inspired by the exemplary results obtained via automatic feature learning and deep learning approaches in computer vision, we focus our attention towards learning salient spatial features via a convolutional neural network (CNN) and then map their temporal relationship with the aid of Long-Short-Term-Memory (LSTM) networks. Our contribution in this paper is a deep fusion framework that more effectively exploits spatial features from CNNs with temporal features from LSTM models. We also extensively evaluate their strengths and weaknesses. We find that by combining both the sets of features, the fully connected features effectively act as an attention mechanism to direct the LSTM to interesting parts of the convolutional feature sequence. The significance of our fusion method is its simplicity and effectiveness compared to other state-of-the-art methods. The evaluation results demonstrate that this hierarchical multi stream fusion method has higher performance compared to single stream mapping methods allowing it to achieve high accuracy outperforming current state-of-the-art methods in three widely used databases: UCF11, UCFSports, jHMDB.

Connected through hyperedge HE-1 (induced by source paper "automatic video object segmentation via motion appearance stream fusion and instance aware segmentation"):
  with members:
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "maskrnn instance level video object segmentation"
    - "video object segmentation with re identification"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "coupled generative adversarial network for continuous fine grained action segmentation"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "stabilizing training of generative adversarial networks through regularization"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-3 (induced by source paper "an attention based recurrent convolutional network for vehicle taillight recognition"):
  with members:
    - "self driving cars a survey"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "convolutional sequence to sequence learning"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "on flow profile image for video representation"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "rank pooling for action recognition"
    - "eigen evolution pooling for human action recognition"
    - "aggregated residual transformations for deep neural networks"
    - "action recognition with dynamic image networks"
    - "convolutional two stream network fusion for video action recognition"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-5 (induced by source paper "forecasting future action sequences with neural memory networks"):
  with members:
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "anticipation and next action forecasting in video an end to end model with memory"
    - "when will you do what anticipating temporal occurrences of activities"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "tree memory networks for modelling long term temporal dependencies"
    - "encouraging lstms to anticipate actions very early"
    - "red reinforced encoder decoder networks for action anticipation"
    - "segmental spatiotemporal cnns for fine grained action segmentation"

Connected through hyperedge HE-6 (induced by source paper "a proposed artificial intelligence model for real time human action localization and tracking"):
  with members:
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "online human action detection using joint classification regression recurrent neural networks"
    - "deep learning for detecting multiple space time action tubes in videos"
    - "online real time multiple spatiotemporal action localisation and prediction"
    - "real time online action detection forests using spatio temporal contexts"
    - "finding action tubes"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "tree memory networks for modelling long term temporal dependencies"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "a deep multi level network for saliency prediction"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "pixel level domain transfer"
    - "energy based generative adversarial network"

Connected through hyperedge HE-8 (induced by source paper "multi level sequence gan for group activity recognition"):
  with members:
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "structural recurrent neural network srnn for group activity analysis"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "cern confidence energy recurrent network for group activity recognition"
    - "pixel level domain transfer"

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)