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 "knowledge distillation for incremental learning in semantic segmentation"):
  Abstract: Although deep learning architectures have shown remarkable results in scene understanding problems, they exhibit a critical drop of overall performance due to catastrophic forgetting when they are required to incrementally learn to recognize new classes without forgetting the old ones. This phenomenon impacts on the deployment of artificial intelligence in real world scenarios where systems need to learn new and different representations over time. Current approaches for incremental learning deal only with the image classification and object detection tasks. In this work we formally introduce the incremental learning problem for semantic segmentation. To avoid catastrophic forgetting we propose to distill the knowledge of the previous model to retain the information about previously learned classes, whilst updating the current model to learn the new ones. We developed three main methodologies of knowledge distillation working on both the output layers and the internal feature representations. Furthermore, differently from other recent frameworks, we do not store any image belonging to the previous training stages while only the last model is used to preserve high accuracy on previously learned classes. Extensive results were conducted on the Pascal VOC2012 dataset and show the effectiveness of the proposed approaches in different incremental learning scenarios.

Includes members:
  Member "incremental learning techniques for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modeling the background for incremental learning in semantic segmentation")
  Member "recent progress in semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "real time freespace segmentation on autonomous robots for detection of obstacles and drop offs")
  Member "large scale incremental learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "scail classifier weights scaling for class incremental learning")
  Member "adversarial learning and self teaching techniques for domain adaptation in semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment")
  Member "similarity preserving knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "group sparsity the hinge between filter pruning and decomposition for network compression")
  Member "learning without memorizing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "continual universal object detection")
  Member "incremental classifier learning with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "brain inspired model for incremental learning using a few examples")

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)