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: "on the role of neurogenesis in overcoming catastrophic forgetting"
  Abstract: Lifelong learning capabilities are crucial for artificial autonomous agents operating on real-world data, which is typically non-stationary and temporally correlated. In this work, we demonstrate that dynamically grown networks outperform static networks in incremental learning scenarios, even when bounded by the same amount of memory in both cases. Learning is unsupervised in our models, a condition that additionally makes training more challenging whilst increasing the realism of the study, since humans are able to learn without dense manual annotation. Our results on artificial neural networks reinforce that structural plasticity constitutes effective prevention against catastrophic forgetting in non-stationary environments, as well as empirically supporting the importance of neurogenesis in the mammalian brain.

Connected through hyperedge HE-1 (induced by source paper "human action recognition and assessment via deep neural network self organization"):
  with members:
    - "rethinking continual learning for autonomous agents and robots"
    - "continual lifelong learning with neural networks a review"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "an incremental self organizing architecture for sensorimotor learning and prediction"
    - "a self organizing neural network architecture for learning human object interactions"
    - "continual learning through synaptic intelligence"
    - "icarl incremental classifier and representation learning"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-2 (induced by source paper "continual lifelong learning with neural networks a review"):
  with members:
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "continuous learning in single incremental task scenarios"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "a neurorobotic experiment for crossmodal conflict resolution in complex environments"
    - "deep generative dual memory network for continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "expectation learning for adaptive crossmodal stimuli association"
    - "automated curriculum learning for neural networks"

Connected through hyperedge HE-3 (induced by source paper "compacting picking and growing for unforgetting continual learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "a comprehensive application oriented study of catastrophic forgetting in dnns"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "learning to remember a synaptic plasticity driven framework for continual learning"
    - "learning without memorizing"
    - "task free continual learning"
    - "incremental classifier learning with generative adversarial networks"
    - "houdini lifelong learning as program synthesis"

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)