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: "knowledge transfer in deep block modular neural networks"
  Abstract: Although deep neural networks (DNNs) have demonstrated impressive results during the last decade, they remain highly specialized tools, which are trained -- often from scratch -- to solve each particular task. The human brain, in contrast, significantly re-uses existing capacities when learning to solve new tasks. In the current study we explore a block-modular architecture for DNNs, which allows parts of the existing network to be re-used to solve a new task without a decrease in performance when solving the original task. We show that networks with such architectures can outperform networks trained from scratch, or perform comparably, while having to learn nearly 10 times fewer weights than the networks trained from scratch.

Connected through hyperedge HE-1 (induced by source paper "brain inspired model for incremental learning using a few examples"):
  with members:
    - "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"
    - "end to end incremental learning"

Connected through hyperedge HE-2 (induced by source paper "residual continual learning"):
  with members:
    - "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"
    - "overcoming catastrophic forgetting by incremental moment matching"

Connected through hyperedge HE-3 (induced by source paper "continual learning in neural networks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "meta learning representations for continual learning"
    - "continual reinforcement learning in 3d non stationary environments"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "experience replay for continual learning"

Connected through hyperedge HE-4 (induced by source paper "a review of modularization techniques in artificial neural networks"):
  with members:
    - "graph hypernetworks for neural architecture search"
    - "mattnet modular attention network for referring expression comprehension"
    - "relational inductive biases deep learning and graph networks"
    - "a graph to sequence model for amr to text generation"
    - "dense and diverse capsule networks making the capsules learn better"
    - "evolving deep neural networks"
    - "hierarchical semantic segmentation using modular convolutional neural networks"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-5 (induced by source paper "growing a brain fine tuning by increasing model capacity"):
  with members:
    - "progressive neural networks"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "a growing long term episodic semantic memory"
    - "one shot learning with memory augmented neural networks"
    - "learning feed forward one shot learners"
    - "cross stitch networks for multi task learning"
    - "what makes imagenet good for transfer learning"
    - "good practice in cnn feature transfer"

Connected through hyperedge HE-6 (induced by source paper "incremental learning using a grow and prune paradigm with efficient neural networks"):
  with members:
    - "hardware guided symbiotic training for compact accurate yet execution efficient lstm"
    - "scann synthesis of compact and accurate neural networks"
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "neural voice cloning with a few samples"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "incremental training of deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "variational federated multi task learning"):
  with members:
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "oracle order robust adaptive continual learning"
    - "applied federated learning improving google keyboard query suggestions"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "memory replay gans learning to generate images from new categories without forgetting"

Connected through hyperedge HE-8 (induced by source paper "superposition of many models into one"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "rethinking the value of network pruning"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "progressive neural networks"
    - "continual learning through synaptic intelligence"

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.NE(Neural and Evolutionary Computing)