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: "continual learning via neural pruning"
  Abstract: We introduce Continual Learning via Neural Pruning (CLNP), a new method aimed at lifelong learning in fixed capacity models based on neuronal model sparsification. In this method, subsequent tasks are trained using the inactive neurons and filters of the sparsified network and cause zero deterioration to the performance of previous tasks. In order to deal with the possible compromise between model sparsity and performance, we formalize and incorporate the concept of graceful forgetting: the idea that it is preferable to suffer a small amount of forgetting in a controlled manner if it helps regain network capacity and prevents uncontrolled loss of performance during the training of future tasks. CLNP also provides simple continual learning diagnostic tools in terms of the number of free neurons left for the training of future tasks as well as the number of neurons that are being reused. In particular, we see in experiments that CLNP verifies and automatically takes advantage of the fact that the features of earlier layers are more transferable. We show empirically that CLNP leads to significantly improved results over current weight elasticity based methods.

Connected through hyperedge HE-1 (induced by source paper "multi scale weight sharing network for image recognition"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "object counting with small datasets of large images"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "aggregated residual transformations for deep neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "densely connected convolutional networks"
    - "wide residual networks"

Connected through hyperedge HE-2 (induced by source paper "unsupervised progressive learning and the stam architecture"):
  with members:
    - "lifelong machine learning with deep streaming linear discriminant analysis"
    - "continual lifelong learning with neural networks a review"
    - "three scenarios for continual learning"
    - "continual learning with tiny episodic memories"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-3 (induced by source paper "continual unsupervised representation learning"):
  with members:
    - "unsupervised continual learning and self taught associative memory hierarchies"
    - "continual lifelong learning with neural networks a review"
    - "three scenarios for continual learning"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "efficient lifelong learning with a gem"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "dirichlet variational autoencoder"

Connected through hyperedge HE-4 (induced by source paper "bayesian structure adaptation for continual learning"):
  with members:
    - "unsupervised continual learning and self taught associative memory hierarchies"
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "nonparametric bayesian deep networks with local competition"
    - "reinforced continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-5 (induced by source paper "nonparametric bayesian structure adaptation for continual learning"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "don t forget there is more than forgetting new metrics for continual learning"
    - "implicit reparameterization gradients"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "lifelong learning with dynamically expandable networks"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "gradient episodic memory for continual learning"

Connected through hyperedge HE-6 (induced by source paper "emergent structures and lifetime structure evolution in artificial neural networks"):
  with members:
    - "understanding deep architectures using a recursive convolutional network"
    - "discriminative recurrent sparse auto encoders"

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)