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: "okutama action an aerial view video dataset for concurrent human action detection"
  Abstract: Despite significant progress in the development of human action detection datasets and algorithms, no current dataset is representative of real-world aerial view scenarios. We present Okutama-Action, a new video dataset for aerial view concurrent human action detection. It consists of 43 minute-long fully-annotated sequences with 12 action classes. Okutama-Action features many challenges missing in current datasets, including dynamic transition of actions, significant changes in scale and aspect ratio, abrupt camera movement, as well as multi-labeled actors. As a result, our dataset is more challenging than existing ones, and will help push the field forward to enable real-world applications.

Connected through hyperedge HE-1 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects"):
  with members:
    - "learning to calibrate straight lines for fisheye image rectification"
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "resource allocation for solar powered uav communication systems"
    - "throughput maximization for laser powered uav wireless communication systems"
    - "learning attraction field representation for robust line segment detection"
    - "anisotropic scale junction detection and matching for indoor images"
    - "deepmvs learning multi view stereopsis"
    - "the uavid dataset for video semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos"):
  with members:
    - "object detection in optical remote sensing images a survey and a new benchmark"
    - "drone based joint density map estimation localization and tracking with space time multi scale attention network"
    - "half a percent of labels is enough efficient animal detection in uav imagery using deep cnns and active learning"
    - "deep learning based aerial image classification for emergency response applications using unmanned aerial vehicles"
    - "a survey of deep learning based object detection"
    - "r 3 net a deep network for multioriented vehicle detection in aerial images and videos"
    - "mrcnet crowd counting and density map estimation in aerial and ground imagery"
    - "detecting mammals in uav images best practices to address a substantially imbalanced dataset with deep learning"

Connected through hyperedge HE-3 (induced by source paper "vehicle re identification in aerial imagery dataset and approach"):
  with members:
    - "uav gesture a dataset for uav control and gesture recognition"
    - "learning rich features for image manipulation detection"
    - "vision meets drones a challenge"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "ram a region aware deep model for vehicle re identification"
    - "distractor aware siamese networks for visual object tracking"

Connected through hyperedge HE-4 (induced by source paper "human action recognition in drone videos using a few aerial training examples"):
  with members:
    - "uav gesture a dataset for uav control and gesture recognition"
    - "real world anomaly detection in surveillance videos"
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "disjoint multi task learning between heterogeneous human centric tasks"
    - "human motion capture using a drone"
    - "learning object localization and 6d pose estimation from simulation and weakly labeled real images"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-5 (induced by source paper "multiple human tracking using multi cues including primitive action features"):
  with members:
    - "deep cosine metric learning for person re identification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "action tubelet detector for spatio temporal action localization"
    - "simple online and realtime tracking with a deep association metric"
    - "tube convolutional neural network t cnn for action detection in videos"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-6 (induced by source paper "congestion analysis of convolutional neural network based pedestrian counting methods on helicopter footage"):
  with members:
    - "vision meets drones a challenge"
    - "yolov3 an incremental improvement"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "a survey of recent advances in cnn based single image crowd counting and density estimation"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "feature pyramid networks for object detection"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-7 (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"
    - "convolutional pose machines"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "review of action recognition and detection methods"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "p cnn pose based cnn features for action recognition"

Connected through hyperedge HE-8 (induced by source paper "adaptive feature processing for robust human activity recognition on a novel multi modal dataset"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "deepcut joint subset partition and labeling for multi person pose estimation"
    - "every moment counts dense detailed labeling of actions in complex videos"
    - "histogram of oriented principal components for cross view action recognition"
    - "fine grained activity recognition with holistic and pose based features"

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)