human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "distillation techniques for pseudo rehearsal based incremental learning"
  Abstract: The ability to learn from incrementally arriving data is essential for any life-long learning system. However, standard deep neural networks forget the knowledge about the old tasks, a phenomenon called catastrophic forgetting, when trained on incrementally arriving data. We discuss the biases in current Generative Adversarial Networks (GAN) based approaches that learn the classifier by knowledge distillation from previously trained classifiers. These biases cause the trained classifier to perform poorly. We propose an approach to remove these biases by distilling knowledge from the classifier of AC-GAN. Experiments on MNIST and CIFAR10 show that this method is comparable to current state of the art rehearsal based approaches. The code for this paper is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "marginal replay vs conditional replay for continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "a comprehensive application oriented study of catastrophic forgetting in dnns"
    - "training discriminative models to evaluate generative ones"
    - "incremental classifier learning with generative adversarial networks"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "keep and learn continual learning by constraining the latent space for knowledge preservation in neural networks"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "deep generative dual memory network for continual learning"

Connected through hyperedge HE-2 (induced by source paper "generative models from the perspective of continual learning"):
  with members:
    - "training discriminative models to evaluate generative ones"
    - "incremental classifier learning with generative adversarial networks"
    - "pros and cons of gan evaluation measures"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "continual learning in generative adversarial nets"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "a strategy for an uncompromising incremental learner"

Question: Please tell me which class the center node 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)