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 interference by conceptors"
  Abstract: Catastrophic interference has been a major roadblock in the research of continual learning. Here we propose a variant of the back-propagation algorithm, "conceptor-aided back-prop" (CAB), in which gradients are shielded by conceptors against degradation of previously learned tasks. Conceptors have their origin in reservoir computing, where they have been previously shown to overcome catastrophic forgetting. CAB extends these results to deep feedforward networks. On the disjoint MNIST task CAB outperforms two other methods for coping with catastrophic interference that have recently been proposed in the deep learning field.

Connected through hyperedge HE-1 (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"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"

Connected through hyperedge HE-2 (induced by source paper "overcoming multi model forgetting"):
  with members:
    - "reinforced continual learning"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "large scale evolution of image classifiers"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "efficient architecture search by network transformation"
    - "hierarchical representations for efficient architecture search"

Connected through hyperedge HE-3 (induced by source paper "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"):
  with members:
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "continual learning through synaptic intelligence"
    - "diffusion based neuromodulation can eliminate catastrophic forgetting in simple neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "one shot learning with memory augmented neural networks"

Connected through hyperedge HE-4 (induced by source paper "overcoming catastrophic forgetting with hard attention to the task"):
  with members:
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "on the origin of deep learning"
    - "continual learning with deep generative replay"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

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)