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: "robust fruit counting combining deep learning tracking and structure from motion"
  Abstract: We present a novel fruit counting pipeline that combines deep segmentation, frame to frame tracking, and 3D localization to accurately count visible fruits across a sequence of images. Our pipeline works on image streams from a monocular camera, both in natural light, as well as with controlled illumination at night. We first train a Fully Convolutional Network (FCN) and segment video frame images into fruit and non-fruit pixels. We then track fruits across frames using the Hungarian Algorithm where the objective cost is determined from a Kalman Filter corrected Kanade-Lucas-Tomasi (KLT) Tracker. In order to correct the estimated count from tracking process, we combine tracking results with a Structure from Motion (SfM) algorithm to calculate relative 3D locations and size estimates to reject outliers and double counted fruit tracks. We evaluate our algorithm by comparing with ground-truth human-annotated visual counts. Our results demonstrate that our pipeline is able to accurately and reliably count fruits across image sequences, and the correction step can significantly improve the counting accuracy and robustness. Although discussed in the context of fruit counting, our work can extend to detection, tracking, and counting of a variety of other stationary features of interest such as leaf-spots, wilt, and blossom.

Connected through hyperedge HE-1 (induced by source paper "a comparative study of fruit detection and counting methods for yield mapping in apple orchards"):
  with members:
    - "semantic mapping for orchard environments by merging two sides reconstructions of tree rows"
    - "focal loss for dense object detection"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "feature pyramid networks for object detection"
    - "image segmentation for fruit detection and yield estimation in apple orchards"
    - "deep fruit detection in orchards"

Connected through hyperedge HE-2 (induced by source paper "minneapple a benchmark dataset for apple detection and segmentation"):
  with members:
    - "a comparative study of fruit detection and counting methods for yield mapping in apple orchards"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "vision based preharvest yield mapping for apple orchards"
    - "focal loss for dense object detection"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "feature pyramid networks for object detection"
    - "deep fruit detection in orchards"

Connected through hyperedge HE-3 (induced by source paper "monocular camera based fruit counting and mapping with semantic data association"):
  with members:
    - "semantic mapping for orchard environments by merging two sides reconstructions of tree rows"
    - "vision based preharvest yield mapping for apple orchards"
    - "fruit quantity and quality estimation using a robotic vision system"
    - "image matching using sift surf brief and orb performance comparison for distorted images"
    - "image segmentation for fruit detection and yield estimation in apple orchards"
    - "deep fruit detection in orchards"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "geomorphological analysis using unpiloted aircraft systems structure from motion and deep learning"):
  with members:
    - "monocular camera based fruit counting and mapping with semantic data association"
    - "mask r cnn"
    - "feature pyramid networks for object detection"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"

Connected through hyperedge HE-5 (induced by source paper "multi robot deep reinforcement learning with macro actions"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "weighted double deep multiagent reinforcement learning in stochastic cooperative environments"
    - "counterfactual multi agent policy gradients"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-6 (induced by source paper "learning multi robot decentralized macro action based policies via a centralized q net"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "value decomposition networks for cooperative multi agent learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q learning"

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)