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 catastrophic forgetting by incremental moment matching"
  Abstract: Catastrophic forgetting is a problem of neural networks that loses the information of the first task after training the second task. Here, we propose a method, i.e. incremental moment matching (IMM), to resolve this problem. IMM incrementally matches the moment of the posterior distribution of the neural network which is trained on the first and the second task, respectively. To make the search space of posterior parameter smooth, the IMM procedure is complemented by various transfer learning techniques including weight transfer, L2-norm of the old and the new parameter, and a variant of dropout with the old parameter. We analyze our approach on a variety of datasets including the MNIST, CIFAR-10, Caltech-UCSD-Birds, and Lifelog datasets. The experimental results show that IMM achieves state-of-the-art performance by balancing the information between an old and a new network.

Connected through hyperedge HE-1 (induced by source paper "ternary feature masks continual learning without any forgetting"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "efficient lifelong learning with a gem"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"

Connected through hyperedge HE-2 (induced by source paper "brain inspired model for incremental learning using a few examples"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "large scale incremental learning"
    - "centroid based scene classification cbsc using deep features and clustering for rgb d indoor scene classification"
    - "cbcl brain inspired model for rgb d indoor scene classification"
    - "learning without memorizing"
    - "incremental classifier learning with generative adversarial networks"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "deesil deep shallow incremental learning"

Connected through hyperedge HE-3 (induced by source paper "residual continual learning"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "continual learning with deep generative replay"
    - "incremental learning through deep adaptation"
    - "learning multiple visual domains with residual adapters"
    - "continual learning through synaptic intelligence"

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"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "end to end incremental learning"
    - "continual learning in generative adversarial nets"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-5 (induced by source paper "dissecting catastrophic forgetting in continual learning by deep visualization"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "contcap a comprehensive framework for continual image captioning"
    - "a unified framework for lifelong learning in deep neural networks"
    - "toward understanding catastrophic forgetting in continual learning"
    - "lifelong gan continual learning for conditional image generation"
    - "incremental learning for semantic segmentation of large scale remote sensing data"
    - "an empirical study of example forgetting during deep neural network learning"
    - "progressive neural networks"

Connected through hyperedge HE-6 (induced by source paper "federated continual learning with adaptive parameter communication"):
  with members:
    - "federated learning with matched averaging"
    - "scalable and order robust continual learning with additive parameter decomposition"
    - "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation"
    - "asynchronous online federated learning for edge devices"
    - "federated optimization for heterogeneous networks"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-7 (induced by source paper "scalable and order robust continual learning with additive parameter decomposition"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "unsupervised representation learning by predicting image rotations"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"

Connected through hyperedge HE-8 (induced by source paper "orthogonal gradient descent for continual learning"):
  with members:
    - "prototype reminding for continual learning"
    - "uncertainty guided continual learning with bayesian neural networks"
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "towards federated learning at scale system design"
    - "variational prototype replays for continual learning"
    - "attention based structural plasticity"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"

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)