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 "random path selection for incremental learning"):
  Abstract: Incremental life-long learning is a main challenge towards the long-standing goal of Artificial General Intelligence. In real-life settings, learning tasks arrive in a sequence and machine learning models must continually learn to increment already acquired knowledge. Existing incremental learning approaches, fall well below the state-of-the-art cumulative models that use all training classes at once. In this paper, we propose a random path selection algorithm, called RPSnet, that progressively chooses optimal paths for the new tasks while encouraging parameter sharing and reuse. Our approach avoids the overhead introduced by computationally expensive evolutionary and reinforcement learning based path selection strategies while achieving considerable performance gains. As an added novelty, the proposed model integrates knowledge distillation and retrospection along with the path selection strategy to overcome catastrophic forgetting. In order to maintain an equilibrium between previous and newly acquired knowledge, we propose a simple controller to dynamically balance the model plasticity. Through extensive experiments, we demonstrate that the proposed method surpasses the state-of-the-art performance on incremental learning and by utilizing parallel computation this method can run in constant time with nearly the same efficiency as a conventional deep convolutional neural network.

Includes members:
  Member "class incremental learning via deep model consolidation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ternary feature masks continual learning without any forgetting")
  Member "continual lifelong learning with neural networks a review":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "exploring randomly wired neural networks for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "re evaluating continual learning scenarios a categorization and case for strong baselines":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a neural dirichlet process mixture model for task free continual learning")
  Member "deep generative dual memory network for continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an adaptive random path selection approach for incremental learning")
  Member "riemannian walk for incremental learning understanding forgetting and intransigence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "generative replay with feedback connections as a general strategy for continual learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a neural dirichlet process mixture model for task free continual learning")
  Member "efficient neural architecture search via parameter sharing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "meta learning curiosity algorithms")

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)