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 dynamic graph lstm for action driven video object detection"
  Abstract: In this paper, we investigate a weakly-supervised object detection framework. Most existing frameworks focus on using static images to learn object detectors. However, these detectors often fail to generalize to videos because of the existing domain shift. Therefore, we investigate learning these detectors directly from boring videos of daily activities. Instead of using bounding boxes, we explore the use of action descriptions as supervision since they are relatively easy to gather. A common issue, however, is that objects of interest that are not involved in human actions are often absent in global action descriptions known as "missing label". To tackle this problem, we propose a novel temporal dynamic graph Long Short-Term Memory network (TD-Graph LSTM). TD-Graph LSTM enables global temporal reasoning by constructing a dynamic graph that is based on temporal correlations of object proposals and spans the entire video. The missing label issue for each individual frame can thus be significantly alleviated by transferring knowledge across correlated objects proposals in the whole video. Extensive evaluations on a large-scale daily-life action dataset (i.e., Charades) demonstrates the superiority of our proposed method. We also release object bounding-box annotations for more than 5,000 frames in Charades. We believe this annotated data can also benefit other research on video-based object recognition in the future.

Connected through hyperedge HE-1 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks"):
  with members:
    - "marginalized average attentional network for weakly supervised learning"
    - "dynamic graph modules for modeling higher order interactions in activity recognition"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "w talc weakly supervised temporal activity localization and classification"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph modules for modeling object object interactions in activity recognition"
    - "a structured model for action detection"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised visual semantic parsing"):
  with members:
    - "from recognition to cognition visual commonsense reasoning"
    - "linknet relational embedding for scene graph"
    - "iterative visual reasoning beyond convolutions"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "learning human object interactions by graph parsing neural networks"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "explainable and explicit visual reasoning over scene graphs"

Connected through hyperedge HE-3 (induced by source paper "dynamic graph convolutional networks"):
  with members:
    - "automated pruning for deep neural network compression"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "structural rnn deep learning on spatio temporal graphs"

Connected through hyperedge HE-4 (induced by source paper "activity driven weakly supervised object detection"):
  with members:
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "zigzag learning for weakly supervised object detection"
    - "adversarial complementary learning for weakly supervised object localization"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "joint discovery of object states and manipulation actions"
    - "detecting and recognizing human object interactions"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-5 (induced by source paper "neural message passing on hybrid spatio temporal visual and symbolic graphs for video understanding"):
  with members:
    - "dynamic graph modules for modeling higher order interactions in activity recognition"
    - "iterative visual reasoning beyond convolutions"
    - "structured label inference for visual understanding"
    - "actor centric relation network"
    - "video action transformer network"
    - "hybrid knowledge routed modules for large scale object detection"
    - "a structured model for action detection"
    - "learning human object interactions by graph parsing neural networks"

Connected through hyperedge HE-6 (induced by source paper "marginalized average attentional network for weakly supervised learning"):
  with members:
    - "adversarial complementary learning for weakly supervised object localization"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "structured attention networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "temporal action detection with structured segment networks"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-7 (induced by source paper "sr lstm state refinement for lstm towards pedestrian trajectory prediction"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "videos as space time region graphs"
    - "vip cnn visual phrase guided convolutional neural network"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "uncertainty aware audiovisual activity recognition using deep bayesian variational inference"):
  with members:
    - "deep audio visual speech recognition"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "moments in time dataset one million videos for event understanding"
    - "learning to separate object sounds by watching unlabeled video"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "audio visual scene analysis with self supervised multisensory features"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

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)