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: "review of action recognition and detection methods"
  Abstract: In computer vision, action recognition refers to the act of classifying an action that is present in a given video and action detection involves locating actions of interest in space and/or time. Videos, which contain photometric information (e.g. RGB, intensity values) in a lattice structure, contain information that can assist in identifying the action that has been imaged. The process of action recognition and detection often begins with extracting useful features and encoding them to ensure that the features are specific to serve the task of action recognition and detection. Encoded features are then processed through a classifier to identify the action class and their spatial and/or temporal locations. In this report, a thorough review of various action recognition and detection algorithms in computer vision is provided by analyzing the two-step process of a typical action recognition and detection algorithm: (i) extraction and encoding of features, and (ii) classifying features into action classes. In efforts to ensure that computer vision-based algorithms reach the capabilities that humans have of identifying actions irrespective of various nuisance variables that may be present within the field of view, the state-of-the-art methods are reviewed and some remaining problems are addressed in the final chapter.

Connected through hyperedge HE-1 (induced by source paper "oops predicting unintentional action in video"):
  with members:
    - "learning video representations using contrastive bidirectional transformer"
    - "self supervised learning for video correspondence flow"
    - "gaze360 physically unconstrained gaze estimation in the wild"
    - "learning correspondence from the cycle consistency of time"
    - "joint task self supervised learning for temporal correspondence"
    - "learning representations for predicting future activities"
    - "revisiting self supervised visual representation learning"
    - "back to the future knowledge distillation for human action anticipation"

Connected through hyperedge HE-2 (induced by source paper "lpat learning to predict adaptive threshold for weakly supervised temporal action localization"):
  with members:
    - "dmc net generating discriminative motion cues for fast compressed video action recognition"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "cascaded pyramid mining network for weakly supervised temporal action localization"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"

Connected through hyperedge HE-3 (induced by source paper "zero shot action recognition in videos a survey"):
  with members:
    - "action2vec a crossmodal embedding approach to action learning"
    - "human action recognition and prediction a survey"
    - "fine grained activity recognition in baseball videos"
    - "towards universal representation for unseen action recognition"
    - "a short note about kinetics 600"
    - "graph convolutional networks for text classification"
    - "visual data synthesis via gan for zero shot video classification"
    - "cross modal and hierarchical modeling of video and text"

Connected through hyperedge HE-4 (induced by source paper "uav gesture a dataset for uav control and gesture recognition"):
  with members:
    - "fine grained activity recognition in baseball videos"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "attentional pooling for action recognition"
    - "okutama action an aerial view video dataset for concurrent human action detection"
    - "convolutional pose machines"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "p cnn pose based cnn features for action recognition"

Connected through hyperedge HE-5 (induced by source paper "autoloc weakly supervised temporal action localization"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "predictive corrective networks for action detection"
    - "temporal action detection with structured segment networks"
    - "deep self taught learning for weakly supervised object localization"
    - "two phase learning for weakly supervised object localization"

Connected through hyperedge HE-6 (induced by source paper "online action detection in untrimmed streaming videos modeling and evaluation"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "predictive corrective networks for action detection"
    - "temporal action detection with structured segment networks"
    - "cascaded boundary regression for temporal action detection"
    - "temporal context network for activity localization in videos"
    - "adversarial discriminative domain adaptation"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-7 (induced by source paper "learning motion in feature space locally consistent deformable convolution networks for fine grained action detection"):
  with members:
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deformable convolutional networks"
    - "dilated residual networks"
    - "temporal convolutional networks for action segmentation and detection"
    - "convolutional two stream network fusion for video action recognition"
    - "segmental spatiotemporal cnns for fine grained action segmentation"

Connected through hyperedge HE-8 (induced by source paper "activity recognition on a large scale in short videos moments in time dataset"):
  with members:
    - "a closer look at weak label learning for audio events"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "temporal relational reasoning in videos"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
    - "cuhk ethz siat submission to activitynet challenge 2016"
    - "word2visualvec image and video to sentence matching by visual feature prediction"
    - "convolutional two stream network fusion for video 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)