human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "monocular camera based fruit counting and mapping with semantic data association"):
  Abstract: In this letter, we present a cheap, lightweight, and fast fruit counting pipeline. Our pipeline relies only on a monocular camera, and achieves counting performance comparable to a state-of-the-art fruit counting system that utilizes an expensive sensor suite including a monocular camera, LiDAR and GPS/INS on a mango dataset. Our pipeline begins with a fruit and tree trunk detection component that uses state-of-the-art convolutional neural networks (CNNs). It then tracks fruits and tree trunks across images, with a Kalman Filter fusing measurements from the CNN detectors and an optical flow estimator. Finally, fruit count and map are estimated by an efficient fruit-as-feature semantic structure from motion algorithm that converts two-dimensional (2-D) tracks of fruits and trunks into 3-D landmarks, and uses these landmarks to identify double counting scenarios. There are many benefits of developing such a low cost and lightweight fruit counting system, including applicability to agriculture in developing countries, where monetary constraints or unstructured environments necessitate cheaper hardware solutions.

Includes members:
  Member "semantic mapping for orchard environments by merging two sides reconstructions of tree rows":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ellipse r cnn learning to infer elliptical object from clustering and occlusion")
  Member "robust fruit counting combining deep learning tracking and structure from motion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a comparative study of fruit detection and counting methods for yield mapping in apple orchards")
  Member "vision based preharvest yield mapping for apple orchards":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semantic mapping for orchard environments by merging two sides reconstructions of tree rows")
  Member "fruit quantity and quality estimation using a robotic vision system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the use of agricultural robots in orchard management")
  Member "image matching using sift surf brief and orb performance comparison for distorted images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "estimating a null model of scientific image reuse to support research integrity investigations")
  Member "image segmentation for fruit detection and yield estimation in apple orchards":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semantic mapping for orchard environments by merging two sides reconstructions of tree rows")
  Member "deep fruit detection in orchards":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ellipse r cnn learning to infer elliptical object from clustering and occlusion")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

Question: Please tell me which class the source paper 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.RO(Robotics)