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 "packnet adding multiple tasks to a single network by iterative pruning"):
  Abstract: This paper presents a method for adding multiple tasks to a single deep neural network while avoiding catastrophic forgetting. Inspired by network pruning techniques, we exploit redundancies in large deep networks to free up parameters that can then be employed to learn new tasks. By performing iterative pruning and network re-training, we are able to sequentially "pack" multiple tasks into a single network while ensuring minimal drop in performance and minimal storage overhead. Unlike prior work that uses proxy losses to maintain accuracy on older tasks, we always optimize for the task at hand. We perform extensive experiments on a variety of network architectures and large-scale datasets, and observe much better robustness against catastrophic forgetting than prior work. In particular, we are able to add three fine-grained classification tasks to a single ImageNet-trained VGG-16 network and achieve accuracies close to those of separately trained networks for each task.

Includes members:
  Member "learning efficient convolutional networks through network slimming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "an analysis of deep neural network models for practical applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "crl class representative learning for image classification")
  Member "progressive neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "encoder based lifelong learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "disassembling the dataset a camera alignment mechanism for multiple tasks in person re identification")
  Member "overcoming catastrophic forgetting by incremental moment matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ternary feature masks continual learning without any forgetting")
  Member "incremental learning of object detectors without catastrophic forgetting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "incremental object detection via meta learning")
  Member "pathnet evolution channels gradient descent in super neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  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)