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: "clusternet detecting small objects in large scenes by exploiting spatio temporal information"
  Abstract: Object detection in wide area motion imagery (WAMI) has drawn the attention of the computer vision research community for a number of years. WAMI proposes a number of unique challenges including extremely small object sizes, both sparse and densely-packed objects, and extremely large search spaces (large video frames). Nearly all state-of-the-art methods in WAMI object detection report that appearance-based classifiers fail in this challenging data and instead rely almost entirely on motion information in the form of background subtraction or frame-differencing. In this work, we experimentally verify the failure of appearance-based classifiers in WAMI, such as Faster R-CNN and a heatmap-based fully convolutional neural network (CNN), and propose a novel two-stage spatio-temporal CNN which effectively and efficiently combines both appearance and motion information to significantly surpass the state-of-the-art in WAMI object detection. To reduce the large search space, the first stage (ClusterNet) takes in a set of extremely large video frames, combines the motion and appearance information within the convolutional architecture, and proposes regions of objects of interest (ROOBI). These ROOBI can contain from one to clusters of several hundred objects due to the large video frame size and varying object density in WAMI. The second stage (FoveaNet) then estimates the centroid location of all objects in that given ROOBI simultaneously via heatmap estimation. The proposed method exceeds state-of-the-art results on the WPAFB 2009 dataset by 5-16% for moving objects and nearly 50% for stopped objects, as well as being the first proposed method in wide area motion imagery to detect completely stationary objects.

Connected through hyperedge HE-1 (induced by source paper "reliability validation of learning enabled vehicle tracking"):
  with members:
    - "test metrics for recurrent neural networks"
    - "reasoning about cognitive trust in stochastic multiagent systems"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "detecting and tracking small moving objects in wide area motion imagery wami using convolutional neural networks cnns"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepmutation mutation testing of deep learning systems"

Connected through hyperedge HE-2 (induced by source paper "penet object detection using points estimation in aerial images"):
  with members:
    - "clustered object detection in aerial images"
    - "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"
    - "objects as points"
    - "augmentation for small object detection"
    - "vision meets drones a challenge"
    - "cornernet detecting objects as paired keypoints"
    - "yolo lite a real time object detection algorithm optimized for non gpu computers"
    - "detnet a backbone network for object detection"

Connected through hyperedge HE-3 (induced by source paper "on learning vehicle detection in satellite video"):
  with members:
    - "clustered object detection in aerial images"
    - "error bounded foreground and background modeling for moving object detection in satellite videos"
    - "multi cue vehicle detection for semantic video compression in georegistered aerial videos"
    - "yolov3 an incremental improvement"
    - "detecting tiny moving vehicles in satellite videos"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "moving object detection by detecting contiguous outliers in the low rank representation"

Connected through hyperedge HE-4 (induced by source paper "does time delay feedback matter to small target motion detection against complex dynamic environments"):
  with members:
    - "a directionally selective small target motion detecting visual neural network in cluttered backgrounds"
    - "a robust visual system for small target motion detection against cluttered moving backgrounds"
    - "contrast oriented deep neural networks for salient object detection"
    - "a feedback neural network for small target motion detection in cluttered backgrounds"
    - "perceptual generative adversarial networks for small object detection"
    - "human pose estimation with iterative error feedback"

Connected through hyperedge HE-5 (induced by source paper "efficient object detection in large images using deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "reduced focal loss 1st place solution to xview object detection in satellite imagery"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "using pre training can improve model robustness and uncertainty"
    - "learning to interpret satellite images using wikipedia"
    - "xview objects in context in overhead imagery"
    - "autofocus efficient multi scale inference"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-6 (induced by source paper "deep built structure counting in satellite imagery using attention based re weighting"):
  with members:
    - "xview objects in context in overhead imagery"
    - "exploring models and data for remote sensing image caption generation"
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "detecting and counting tiny faces"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

Connected through hyperedge HE-7 (induced by source paper "clustered object detection in aerial images"):
  with members:
    - "high resolution representations for labeling pixels and regions"
    - "vision meets drones a challenge"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "learning roi transformer for detecting oriented objects in aerial images"
    - "focal loss for dense object detection"
    - "dota a large scale dataset for object detection in aerial images"
    - "cascade r cnn delving into high quality object detection"
    - "single shot refinement neural network for object detection"

Connected through hyperedge HE-8 (induced by source paper "bio inspired foveated technique for augmented range vehicle detection using deep neural networks"):
  with members:
    - "self driving cars a survey"
    - "object detection with deep learning a review"
    - "focal loss for dense object detection"
    - "3d object proposals using stereo imagery for accurate object class detection"
    - "yolo9000 better faster stronger"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "feature pyramid networks for object detection"
    - "multi scale volumes for deep object detection and localization"

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)