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: "sena cnn overcoming catastrophic forgetting in convolutional neural networks by selective network augmentation"
  Abstract: Lifelong learning aims to develop machine learning systems that can learn new tasks while preserving the performance on previous learned tasks. In this paper we present a method to overcome catastrophic forgetting on convolutional neural networks, that learns new tasks and preserves the performance on old tasks without accessing the data of the original model, by selective network augmentation. The experiment results showed that SeNA-CNN, in some scenarios, outperforms the state-of-art Learning without Forgetting algorithm. Results also showed that in some situations it is better to use SeNA-CNN instead of training a neural network using isolated learning.

Connected through hyperedge HE-1 (induced by source paper "overcoming catastrophic forgetting by neuron level plasticity control"):
  with members:
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "deep generative dual memory network for continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "memory aware synapses learning what not to forget"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning with deep generative replay"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-2 (induced by source paper "incremental learning in person re identification"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "multi target multi camera tracking by hierarchical clustering recent progress on dukemtmc project"
    - "a comprehensive evaluation and benchmark for person re identification features metrics and datasets"
    - "learning without forgetting"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "no more pesky learning rate guessing games"
    - "adam a method for stochastic optimization"

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)