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: "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
  Abstract: Humans and most animals can learn new tasks without forgetting old ones. However, training artificial neural networks (ANNs) on new tasks typically causes them to forget previously learned tasks. This phenomenon is the result of “catastrophic forgetting,” in which training an ANN disrupts connection weights that were important for solving previous tasks, degrading task performance. Several recent studies have proposed methods to stabilize connection weights of ANNs that are deemed most important for solving a task, which helps alleviate catastrophic forgetting. Here, drawing inspiration from algorithms that are believed to be implemented in vivo, we propose a complementary method: adding a context-dependent gating signal, such that only sparse, mostly nonoverlapping patterns of units are active for any one task. This method is easy to implement, requires little computational overhead, and allows ANNs to maintain high performance across large numbers of sequentially presented tasks, particularly when combined with weight stabilization. We show that this method works for both feedforward and recurrent network architectures, trained using either supervised or reinforcement-based learning. This suggests that using multiple, complementary methods, akin to what is believed to occur in the brain, can be a highly effective strategy to support continual learning.

Connected through hyperedge HE-1 (induced by source paper "learning to continually learn"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "meta learning representations for continual learning"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "continuous learning in single incremental task scenarios"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "explicit inductive bias for transfer learning with convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "structured compression and sharing of representational space 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"
    - "gradient based sample selection for online continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "a low effort approach to structured cnn design using pca"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-3 (induced by source paper "openloris object a robotic vision dataset and benchmark for lifelong deep learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "generative adversarial networks a survey and taxonomy"
    - "neural dynamics discovery via gaussian process recurrent neural networks"
    - "are we ready for service robots the openloris scene datasets for lifelong slam"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "incremental classifier learning with generative adversarial networks"
    - "the limits and potentials of deep learning for robotics"
    - "don t forget there is more than forgetting new metrics for continual learning"

Connected through hyperedge HE-4 (induced by source paper "adversarial incremental learning"):
  with members:
    - "large scale incremental learning"
    - "attending to discriminative certainty for domain adaptation"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "end to end incremental learning"
    - "continual learning in generative adversarial nets"
    - "memory aware synapses learning what not to forget"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "intrinsic motivation and episodic memories for robot exploration of high dimensional sensory spaces"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "curious intrinsically motivated modular multi goal reinforcement learning"
    - "intrinsically motivated goal exploration processes with automatic curriculum learning"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"

Connected through hyperedge HE-6 (induced by source paper "batch shaping for learning conditional channel gated networks"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "sgad soft guided adaptively dropped neural network"
    - "compressing neural networks using the variational information bottleneck"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "dynamic channel pruning feature boosting and suppression"
    - "skipnet learning dynamic routing in convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "continual learning with hypernetworks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "large scale adversarial representation learning"
    - "three scenarios for continual learning"
    - "high fidelity image generation with fewer labels"
    - "experience replay for continual learning"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "measuring and regularizing networks in function space"

Connected through hyperedge HE-8 (induced by source paper "knowledge infused learning k il towards deep incorporation of knowledge in deep learning"):
  with members:
    - "infusing domain knowledge in ai based black box models for better explainability with application in bankruptcy prediction"
    - "ernie enhanced representation through knowledge integration"
    - "predictive analysis on twitter techniques and applications"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "modeling islamist extremist communications on social media using contextual dimensions religion ideology and hate"
    - "k bert enabling language representation with knowledge graph"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "ai reasoning systems pac and applied methods"

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)