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 "active and incremental learning with weak supervision"):
  Abstract: Large amounts of labeled training data are one of the main contributors to the great success that deep models have achieved in the past. Label acquisition for tasks other than benchmarks can pose a challenge due to requirements of both funding and expertise. By selecting unlabeled examples that are promising in terms of model improvement and only asking for respective labels, active learning can increase the efficiency of the labeling process in terms of time and cost. In this work, we describe combinations of an incremental learning scheme and methods of active learning. These allow for continuous exploration of newly observed unlabeled data. We describe selection criteria based on model uncertainty as well as expected model output change (EMOC). An object detection task is evaluated in a continuous exploration context on the PASCAL VOC dataset. We also validate a weakly supervised system based on active and incremental learning in a real-world biodiversity application where images from camera traps are analyzed. Labeling only 32 images by accepting or rejecting proposals generated by our method yields an increase in accuracy from 25.4 to 42.6%.

Includes members:
  Member "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mild net minimal information loss dilated network for gland instance segmentation in colon histology images")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "active learning for deep object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "temporal coherence for active learning in videos")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "cost effective active learning for deep image classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reinforced active learning for image segmentation")
  Member "extreme clicking for efficient object annotation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "incremental learning of object detectors without catastrophic forgetting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "incremental object detection via meta learning")
  Member "automatically identifying wild animals in camera trap images with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "synthetic examples improve generalization for rare classes")

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)