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: "keep and learn continual learning by constraining the latent space for knowledge preservation in neural networks"
  Abstract: Data is one of the most important factors in machine learning. However, even if we have high-quality data, there is a situation in which access to the data is restricted. For example, access to the medical data from outside is strictly limited due to the privacy issues. In this case, we have to learn a model sequentially only with the data accessible in the corresponding stage. In this work, we propose a new method for preserving learned knowledge by modeling the high-level feature space and the output space to be mutually informative, and constraining feature vectors to lie in the modeled space during training. The proposed method is easy to implement as it can be applied by simply adding a reconstruction loss to an objective function. We evaluate the proposed method on CIFAR-10/100 and a chest X-ray dataset, and show benefits in terms of knowledge preservation compared to previous approaches.

Connected through hyperedge HE-1 (induced by source paper "overcoming catastrophic forgetting with unlabeled data in the wild"):
  with members:
    - "supportnet solving catastrophic forgetting in class incremental learning with support data"
    - "incremental classifier learning with generative adversarial networks"
    - "revisiting distillation and incremental classifier learning"
    - "spectral normalization for generative adversarial networks"
    - "hierarchical novelty detection for visual object recognition"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "exploring the limits of weakly supervised pretraining"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive application oriented study of catastrophic forgetting in dnns"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "continual learning with deep generative replay"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "measuring catastrophic forgetting in neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "fruit recognition from images using deep learning"

Connected through hyperedge HE-3 (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"
    - "distillation techniques for pseudo rehearsal based incremental learning"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "deep generative dual memory network for continual learning"

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)