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: "self delimiting neural networks"
  Abstract: Self-delimiting (SLIM) programs are a central concept of theoretical computer science, particularly algorithmic information & probability theory, and asymptotically optimal program search (AOPS). To apply AOPS to (possibly recurrent) neural networks (NNs), I introduce SLIM NNs. Neurons of a typical SLIM NN have threshold activation functions. During a computational episode, activations are spreading from input neurons through the SLIM NN until the computation activates a special halt neuron. Weights of the NN's used connections define its program. Halting programs form a prefix code. The reset of the initial NN state does not cost more than the latest program execution. Since prefixes of SLIM programs influence their suffixes (weight changes occurring early in an episode influence which weights are considered later), SLIM NN learning algorithms (LAs) should execute weight changes online during activation spreading. This can be achieved by applying AOPS to growing SLIM NNs. To efficiently teach a SLIM NN to solve many tasks, such as correctly classifying many different patterns, or solving many different robot control tasks, each connection keeps a list of tasks it is used for. The lists may be efficiently updated during training. To evaluate the overall effect of currently tested weight changes, a SLIM NN LA needs to re-test performance only on the efficiently computable union of tasks potentially affected by the current weight changes. Future SLIM NNs will be implemented on 3-dimensional brain-like multi-processor hardware. Their LAs will minimize task-specific total wire length of used connections, to encourage efficient solutions of subtasks by subsets of neurons that are physically close. The novel class of SLIM NN LAs is currently being probed in ongoing experiments to be reported in separate papers.

Connected through hyperedge HE-1 (induced by source paper "improving differentiable neural computers through memory masking de allocation and link distribution sharpness control"):
  with members:
    - "robust and scalable differentiable neural computer for question answering"
    - "adaptive computation time for recurrent neural networks"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "tracking the world state with recurrent entity networks"
    - "towards ai complete question answering a set of prerequisite toy tasks"

Connected through hyperedge HE-2 (induced by source paper "layer flexible adaptive computational time for recurrent neural networks"):
  with members:
    - "universal transformers"
    - "depth adaptive computational policies for efficient visual tracking"
    - "dynamic prediction length for time series with sequence to sequence networks"
    - "iamnn iterative and adaptive mobile neural network for efficient image classification"
    - "early improving recurrent elastic highway network"
    - "learning to skim text"
    - "dynamic computational time for visual attention"
    - "learning to reason with adaptive computation"

Connected through hyperedge HE-3 (induced by source paper "one big net for everything"):
  with members:
    - "progressive reinforcement learning with distillation for multi skilled motion control"
    - "progressive neural networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep learning in neural networks"
    - "on learning to think algorithmic information theory for novel combinations of reinforcement learning controllers and recurrent neural world models"
    - "show and tell a neural image caption generator"
    - "first experiments with powerplay"
    - "multi column deep neural networks for image classification"

Connected through hyperedge HE-4 (induced by source paper "modular networks learning to decompose neural computation"):
  with members:
    - "learning to compose skills"
    - "learning structured sparsity in deep neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "learning factored representations in a deep mixture of experts"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-5 (induced by source paper "adaptive computation time for recurrent neural networks"):
  with members:
    - "attend infer repeat fast scene understanding with generative models"
    - "conditional computation in neural networks for faster models"
    - "end to end memory networks"
    - "learning to transduce with unbounded memory"
    - "grid long short term memory"
    - "neural programmer interpreters"
    - "draw a recurrent neural network for image generation"
    - "training very deep networks"

Connected through hyperedge HE-6 (induced by source paper "recurrent highway networks"):
  with members:
    - "lstm a search space odyssey"
    - "adaptive computation time for recurrent neural networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "neural architecture search with reinforcement learning"
    - "exploring the limits of language modeling"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "deep learning in neural networks"):
  with members:
    - "going deeper with convolutions"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "a clockwork rnn"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "sequence to sequence learning with neural networks"
    - "multi column deep neural networks for offline handwritten chinese character classification"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "efficient visual coding from retina to v2"

Connected through hyperedge HE-8 (induced by source paper "on learning to think algorithmic information theory for novel combinations of reinforcement learning controllers and recurrent neural world models"):
  with members:
    - "deep learning in neural networks"
    - "going deeper with convolutions"
    - "neural turing machines"
    - "deep speech scaling up end to end speech recognition"
    - "a clockwork rnn"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"
    - "visualizing and understanding convolutional networks"

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)