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: "overcoming multi model forgetting"
  Abstract: We identify a phenomenon, which we refer to as multi-model forgetting, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, due to the overwriting of shared parameters. To overcome this, we introduce a statistically-justified weight plasticity loss that regularizes the learning of a model's shared parameters according to their importance for the previous models, and demonstrate its effectiveness when training two models sequentially and for neural architecture search. Adding weight plasticity in neural architecture search preserves the best models to the end of the search and yields improved results in both natural language processing and computer vision tasks.

Connected through hyperedge HE-1 (induced by source paper "energy aware neural architecture optimization with fast splitting steepest descent"):
  with members:
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ecc platform independent energy constrained deep neural network compression via a bilinear regression model"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-2 (induced by source paper "learning to remember from a multi task teacher"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "meta learning representations for continual learning"
    - "understanding and correcting pathologies in the training of learned optimizers"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "reviving and improving recurrent back propagation"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "packnet adding multiple tasks to a single network by iterative pruning"

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)