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: "a growing long term episodic semantic memory"
  Abstract: The long-term memory of most connectionist systems lies entirely in the weights of the system. Since the number of weights is typically fixed, this bounds the total amount of knowledge that can be learned and stored. Though this is not normally a problem for a neural network designed for a specific task, such a bound is undesirable for a system that continually learns over an open range of domains. To address this, we describe a lifelong learning system that leverages a fast, though non-differentiable, content-addressable memory which can be exploited to encode both a long history of sequential episodic knowledge and semantic knowledge over many episodes for an unbounded number of domains. This opens the door for investigation into transfer learning, and leveraging prior knowledge that has been learned over a lifetime of experiences to new domains.

Connected through hyperedge HE-1 (induced by source paper "growing a brain fine tuning by increasing model capacity"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "progressive neural networks"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "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-2 (induced by source paper "enhanced transfer learning with imagenet trained classification layer"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "learning more universal representations for transfer learning"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning visual features from large weakly supervised data"
    - "multi scale recognition with dag cnns"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "depth augmented networks with optimal fine tuning"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "learning more universal representations for transfer learning"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning visual features from large weakly supervised data"
    - "multi scale recognition with dag cnns"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "a brief study of in domain transfer and learning from fewer samples using a few simple priors"):
  with members:
    - "progressive neural networks"
    - "learning to learn by gradient descent by gradient descent"
    - "latent contextual bandits and their application to personalized recommendations for new users"
    - "overcoming catastrophic forgetting in neural networks"
    - "multi task sequence to sequence learning"
    - "efficient optimal learning for contextual bandits"

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.AI(Artificial Intelligence)