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: "neurogenesis deep learning extending deep networks to accommodate new classes"
  Abstract: Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing — data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "on catastrophic interference in atari 2600 games"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "continual unsupervised representation learning"
    - "continual lifelong learning with neural networks a review"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "benchmarking bonus based exploration methods on the arcade learning environment"
    - "dopamine a research framework for deep reinforcement learning"
    - "exploration by random network distillation"
    - "task free continual learning"

Connected through hyperedge HE-2 (induced by source paper "moving towards open set incremental learning readily discovering new authors"):
  with members:
    - "icarl incremental classifier and representation learning"
    - "towards open set deep networks"
    - "towards open world recognition"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (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"
    - "on the role of neurogenesis in overcoming catastrophic forgetting"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "continual unsupervised representation learning"):
  with members:
    - "unsupervised continual learning and self taught associative memory hierarchies"
    - "continual lifelong learning with neural networks a review"
    - "continual learning via neural pruning"
    - "three scenarios for continual learning"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "efficient lifelong learning with a gem"
    - "life long disentangled representation learning with cross domain latent homologies"

Connected through hyperedge HE-6 (induced by source paper "fast construction of correcting ensembles for legacy artificial intelligence systems algorithms and a case study"):
  with members:
    - "correction of ai systems by linear discriminants probabilistic foundations"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "stochastic separation theorems"
    - "stochastic configuration networks fundamentals and algorithms"
    - "stochastic configuration networks ensemble with heterogeneous features for large scale data analytics"
    - "deep stochastic configuration networks with universal approximation property"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "using deep convolutional networks for gesture recognition in american sign language"

Connected through hyperedge HE-7 (induced by source paper "lifelong learning starting from zero"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "the incredible shrinking neural network new perspectives on learning representations through the lens of pruning"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "continual learning through synaptic intelligence"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-8 (induced by source paper "continuous learning for large scale personalized domain classification"):
  with members:
    - "a scalable neural shortlisting reranking approach for large scale domain classification in natural language understanding"
    - "efficient large scale domain classification with personalized attention"
    - "self normalizing neural networks"
    - "continual learning through synaptic intelligence"
    - "measuring catastrophic forgetting in neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "icarl incremental classifier and representation learning"
    - "effective approaches to attention based neural machine translation"

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)