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: "uav gesture a dataset for uav control and gesture recognition"
  Abstract: Current UAV-recorded datasets are mostly limited to action recognition and object tracking, whereas the gesture signals datasets were mostly recorded in indoor spaces. Currently, there is no outdoor recorded public video dataset for UAV commanding signals. Gesture signals can be effectively used with UAVs by leveraging the UAVs visual sensors and operational simplicity. To fill this gap and enable research in wider application areas, we present a UAV gesture signals dataset recorded in an outdoor setting. We selected 13 gestures suitable for basic UAV navigation and command from general aircraft handling and helicopter handling signals. We provide 119 high-definition video clips consisting of 37151 frames. The overall baseline gesture recognition performance computed using Pose-based Convolutional Neural Network (P-CNN) is 91.9 %. All the frames are annotated with body joints and gesture classes in order to extend the dataset's applicability to a wider research area including gesture recognition, action recognition, human pose recognition and situation awareness.

Connected through hyperedge HE-1 (induced by source paper "implementation of a natural user interface to command a drone"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "accurate indoor mapping using an autonomous unmanned aerial vehicle uav"
    - "densepose dense human pose estimation in the wild"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-2 (induced by source paper "vehicle re identification in aerial imagery dataset and approach"):
  with members:
    - "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"
    - "attention aware compositional network for person re identification"

Connected through hyperedge HE-3 (induced by source paper "human action recognition in drone videos using a few aerial training examples"):
  with members:
    - "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"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-4 (induced by source paper "fitting comparison and alignment of trajectories on positive semi definite matrices with application to action recognition"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "human action recognition and prediction a survey"
    - "deep covariance descriptors for facial expression recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "optimized skeleton based action recognition via sparsified graph regression"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "automatic analysis of facial expressions based on deep covariance trajectories"
    - "skeleton based human action recognition with global context aware attention lstm networks"

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.LG(Machine Learning)