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 "tree species identification from bark images using convolutional neural networks"):
  Abstract: Tree species identification using images of the bark is a challenging problem that could help in tasks such as drone navigation in forest environment and autonomous forest inventory management. It also brings more value to harvesting operations as it leads to greater market values of trees. While the recent progress in deep learning showed its effectiveness for visual classification, it cannot currently be used for bark classification due to a lack of dataset. In this work, we present a novel dataset containing more than 23 000 high-resolution bark images from 23 different species and establish a benchmark using deep learning. We obtain an accuracy of 93.88% and show the possibility of using a majority voting approach on all the images of a tree to obtain 97.81% accuracy. We also perform experiments indicating that it is more important to collect a large amount of trees over a large quantity of image and that images of a single tree should be taken at different locations.

Includes members:
  Member "uav visual teach and repeat using only semantic object features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "there s no place like home visual teach and repeat for emergency return of multirotor uavs during gps failure")
  Member "multi task learning by deep collaboration and application in facial landmark detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discovering differential features adversarial learning for information credibility evaluation")
  Member "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aggressive perception aware navigation using deep optical flow dynamics and pixelmpc")
  Member "a survey on multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery")
  Member "learning rotation invariant convolutional filters for texture classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rest performance improvement of a black box model via rl based spatial transformation")
  Member "relay backpropagation for effective learning of deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "delving deep into rectifiers surpassing human level performance on imagenet classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.CV(Computer Vision and Pattern Recognition)