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: "temporal attention gated model for robust sequence classification"
  Abstract: Typical techniques for sequence classification are designed for well-segmented sequences which have been edited to remove noisy or irrelevant parts. Therefore, such methods cannot be easily applied on noisy sequences expected in real-world applications. In this paper, we present the Temporal Attention-Gated Model (TAGM) which integrates ideas from attention models and gated recurrent networks to better deal with noisy or unsegmented sequences. Specifically, we extend the concept of attention model to measure the relevance of each observation (time step) of a sequence. We then use a novel gated recurrent network to learn the hidden representation for the final prediction. An important advantage of our approach is interpretability since the temporal attention weights provide a meaningful value for the salience of each time step in the sequence. We demonstrate the merits of our TAGM approach, both for prediction accuracy and interpretability, on three different tasks: spoken digit recognition, text-based sentiment analysis and visual event recognition.

Connected through hyperedge HE-1 (induced by source paper "non local recurrent neural memory for supervised sequence modeling"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "relational recurrent neural networks"
    - "high order recurrent neural networks for acoustic modelling"
    - "glomo unsupervisedly learned relational graphs as transferable representations"
    - "learning to generate reviews and discovering sentiment"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "non local neural networks"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised completion moment detection using temporal attention"):
  with members:
    - "action recognition from single timestamp supervision in untrimmed videos"
    - "temporal cycle consistency learning"
    - "action completion a temporal model for moment detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-3 (induced by source paper "discriminative feature learning with foreground attention for person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "person re identification with deep similarity guided graph neural network"
    - "efficient and deep person re identification using multi level similarity"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "human semantic parsing for person re identification"
    - "resource aware person re identification across multiple resolutions"

Connected through hyperedge HE-4 (induced by source paper "deep adaptive temporal pooling for activity recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "efficient and deep person re identification using multi level similarity"
    - "non linear temporal subspace representations for activity recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "attention is all you need"
    - "appearance and relation networks for video classification"

Connected through hyperedge HE-5 (induced by source paper "soccernet a scalable dataset for action spotting in soccer videos"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "temporal action detection with structured segment networks"
    - "learnable pooling with context gating for video classification"
    - "temporal context network for activity localization in videos"
    - "action tubelet detector for spatio temporal action localization"
    - "actionvlad learning spatio temporal aggregation for action classification"

Connected through hyperedge HE-6 (induced by source paper "attention gated networks learning to leverage salient regions in medical images"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "learn to pay attention"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "attention is all you need"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"

Connected through hyperedge HE-7 (induced by source paper "the pros and cons rank aware temporal attention for skill determination in long videos"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning to score the figure skating sports videos"
    - "w talc weakly supervised temporal activity localization and classification"
    - "deep learning with convolutional neural network for objective skill evaluation in robot assisted surgery"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-8 (induced by source paper "attention gated networks for improving ultrasound scan plane detection"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"
    - "learn to pay attention"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "non local neural networks"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "a survey on deep learning in medical image analysis"
    - "residual attention network for image classification"

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)