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 "incremental learning techniques for semantic segmentation"):
  Abstract: Deep learning architectures exhibit a critical drop of performance due to catastrophic forgetting when they are required to incrementally learn new tasks. Contemporary incremental learning frameworks focus on image classification and object detection while in this work we formally introduce the incremental learning problem for semantic segmentation in which a pixel-wise labeling is considered. To tackle this task 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 propose various approaches working both on the output logits and on intermediate features. In opposition to some recent frameworks, we do not store any image from previously learned classes and only the last model is needed to preserve high accuracy on these classes. The experimental evaluation on the Pascal VOC2012 dataset shows the effectiveness of the proposed approaches.

Includes members:
  Member "large scale incremental learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "scail classifier weights scaling for class incremental learning")
  Member "tree cnn a hierarchical deep convolutional neural network for incremental learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")
  Member "learning without memorizing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "continual universal object detection")
  Member "incremental classifier learning with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "brain inspired model for incremental learning using a few examples")
  Member "tree cnn a deep convolutional neural network for lifelong learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "structured compression and sharing of representational space for continual learning")
  Member "incremental learning for semantic segmentation of large scale remote sensing data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "modeling the background for incremental learning in semantic segmentation")
  Member "incremental training of deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neunets an automated synthesis engine for neural network design")

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)