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: "a comprehensive application oriented study of catastrophic forgetting in dnns"
  Abstract: We present a large-scale empirical study of catastrophic forgetting (CF) in modern Deep Neural Network (DNN) models that perform sequential (or: incremental) learning. A new experimental protocol is proposed that enforces typical constraints encountered in application scenarios. As the investigation is empirical, we evaluate CF behavior on the hitherto largest number of visual classification datasets, from each of which we construct a representative number of Sequential Learning Tasks (SLTs) in close alignment to previous works on CF. Our results clearly indicate that there is no model that avoids CF for all investigated datasets and SLTs under application conditions. We conclude with a discussion of potential solutions and workarounds to CF, notably for the EWC and IMM models.

Connected through hyperedge HE-1 (induced by source paper "continual learning a comparative study on how to defy forgetting in classification tasks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "continual learning for robotics"
    - "continual lifelong learning with neural networks a review"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "experience replay for continual learning"

Connected through hyperedge HE-2 (induced by source paper "compacting picking and growing for unforgetting continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "task free continual learning"
    - "incremental classifier learning with generative adversarial networks"
    - "houdini lifelong learning as program synthesis"
    - "on the role of neurogenesis in overcoming catastrophic forgetting"

Connected through hyperedge HE-3 (induced by source paper "gradient based training of gaussian mixture models in high dimensional spaces"):
  with members:
    - "a fast incremental gaussian mixture model"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "marginal replay vs conditional replay for continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "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"
    - "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"

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.LG(Machine Learning)