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: "online contrastive divergence with generative replay experience replay without storing data"
  Abstract: Conceived in the early 1990s, Experience Replay (ER) has been shown to be a successful mechanism to allow online learning algorithms to reuse past experiences. Traditionally, ER can be applied to all machine learning paradigms (i.e., unsupervised, supervised, and reinforcement learning). Recently, ER has contributed to improving the performance of deep reinforcement learning. Yet, its application to many practical settings is still limited by the memory requirements of ER, necessary to explicitly store previous observations. To remedy this issue, we explore a novel approach, Online Contrastive Divergence with Generative Replay (OCD_GR), which uses the generative capability of Restricted Boltzmann Machines (RBMs) instead of recorded past experiences. The RBM is trained online, and does not require the system to store any of the observed data points. We compare OCD_GR to ER on 9 real-world datasets, considering a worst-case scenario (data points arriving in sorted order) as well as a more realistic one (sequential random-order data points). Our results show that in 64.28% of the cases OCD_GR outperforms ER and in the remaining 35.72% it has an almost equal performance, while having a considerably reduced space complexity (i.e., memory usage) at a comparable time complexity.

Connected through hyperedge HE-1 (induced by source paper "generative memory for lifelong reinforcement learning"):
  with members:
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "selective experience replay for lifelong learning"
    - "progressive neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "icarl incremental classifier and representation learning"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-2 (induced by source paper "one shot learning using mixture of variational autoencoders a generalization learning approach"):
  with members:
    - "active one shot learning"
    - "a survey on deep learning in medical image analysis"
    - "nonparametric variational auto encoders for hierarchical representation learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "one shot learning with memory augmented neural networks"
    - "matching networks for one shot learning"
    - "low shot visual object recognition"
    - "learning to see by moving"

Connected through hyperedge HE-3 (induced by source paper "continual learning with deep generative replay"):
  with members:
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "improved training of wasserstein gans"
    - "overcoming catastrophic forgetting in neural networks"
    - "learning without forgetting"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "estimating 3d trajectories from 2d projections via disjunctive factored four way conditional restricted boltzmann machines"):
  with members:
    - "a topological insight into restricted boltzmann machines"
    - "depth map prediction from a single image using a multi scale deep network"

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.LG(Machine Learning)