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 "faster ilod incremental learning for object detectors based on faster rcnn"):
  Abstract: The human vision and perception system is inherently incremental where new knowledge is continually learned over time whilst existing knowledge is retained. On the other hand, deep learning networks are ill-equipped for incremental learning. When a well-trained network is adapted to new categories, its performance on the old categories will dramatically degrade. To address this problem, incremental learning methods have been explored to preserve the old knowledge of deep learning models. However, the state-of-the-art incremental object detector employs an external fixed region proposal method that increases overall computation time and reduces accuracy compared to object detectors such as Faster RCNN that use trainable Region Proposal Networks (RPNs). The purpose of this paper is to design an efficient end-to-end incremental object detector using knowledge distillation for object detectors based on RPNs. We first evaluate and analyze the performance of RPN-based detector with classic distillation towards incremental detection tasks. Then, we introduce multi-network adaptive distillation that properly retains knowledge from the old categories when fine-turning the model for new task. Experiments on the benchmark datasets, PASCAL VOC and COCO, demonstrate that the proposed incremental detector is more accurate as well as being 13 times faster than the baseline detector.

Includes members:
  Member "rilod near real time incremental learning for object detection at the edge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "incremental object detection via meta learning")
  Member "large scale incremental learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modeling the background for incremental learning in semantic segmentation")
  Member "m2kd multi model and multi level knowledge distillation for incremental learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scail classifier weights scaling for class incremental learning")
  Member "soft sampling for robust object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection as a positive unlabeled problem")
  Member "end to end incremental learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "incremental object detection via meta learning")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  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 "icarl incremental classifier and representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")

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)