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: "learning attractor dynamics for generative memory"
  Abstract: A central challenge faced by memory systems is the robust retrieval of a stored pattern in the presence of interference due to other stored patterns and noise. A theoretically well-founded solution to robust retrieval is given by attractor dynamics, which iteratively clean up patterns during recall. However, incorporating attractor dynamics into modern deep learning systems poses difficulties: attractor basins are characterised by vanishing gradients, which are known to make training neural networks difficult. In this work, we avoid the vanishing gradient problem by training a generative distributed memory without simulating the attractor dynamics. Based on the idea of memory writing as inference, as proposed in the Kanerva Machine, we show that a likelihood-based Lyapunov function emerges from maximising the variational lower-bound of a generative memory. Experiments shows it converges to correct patterns upon iterative retrieval and achieves competitive performance as both a memory model and a generative model.

Connected through hyperedge HE-1 (induced by source paper "product kanerva machines factorized bayesian memory"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "monet unsupervised scene decomposition and representation"
    - "generalization of reinforcement learners with working and episodic memory"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "metalearned neural memory"
    - "recurrent independent mechanisms"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "shaping belief states with generative environment models for rl"

Connected through hyperedge HE-2 (induced by source paper "shaping belief states with generative environment models for rl"):
  with members:
    - "conditional adversarial generative flow for controllable image synthesis"
    - "preventing posterior collapse with delta vaes"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "geometric consistency for self supervised end to end visual odometry"
    - "learning and querying fast generative models for reinforcement learning"
    - "deep variational reinforcement learning for pomdps"

Connected through hyperedge HE-3 (induced by source paper "metalearned neural memory"):
  with members:
    - "contextual memory trees"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "learning to reason with third order tensor products"
    - "metalearning with hebbian fast weights"
    - "one shot visual imitation learning via meta learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "rapid adaptation with conditionally shifted neurons"
    - "a simple neural attentive meta learner"

Connected through hyperedge HE-4 (induced by source paper "meta learning neural bloom filters"):
  with members:
    - "optimizing learned bloom filters by sandwiching"
    - "a model for learned bloom filters and related structures"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "in datacenter performance analysis of a tensor processing unit"
    - "the case for learned index structures"
    - "device placement optimization with reinforcement learning"
    - "learning to remember rare events"
    - "scaling memory augmented neural networks with sparse reads and writes"

Connected through hyperedge HE-5 (induced by source paper "convolutional bipartite attractor networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "reviving and improving recurrent back propagation"
    - "fast and accurate image super resolution with deep laplacian pyramid networks"
    - "density estimation using real nvp"
    - "dense associative memory for pattern recognition"
    - "deeply recursive convolutional network for image super resolution"

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)