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: "structured memory for neural turing machines"
  Abstract: Neural Turing Machines (NTM) contain memory component that simulates "working memory" in the brain to store and retrieve information to ease simple algorithms learning. So far, only linearly organized memory is proposed, and during experiments, we observed that the model does not always converge, and overfits easily when handling certain tasks. We think memory component is key to some faulty behaviors of NTM, and better organization of memory component could help fight those problems. In this paper, we propose several different structures of memory for NTM, and we proved in experiments that two of our proposed structured-memory NTMs could lead to better convergence, in term of speed and prediction accuracy on copy task and associative recall task as in (Graves et al. 2014).

Connected through hyperedge HE-1 (induced by source paper "memory augmented recurrent networks for dialogue coherence"):
  with members:
    - "towards universal dialogue state tracking"
    - "a hierarchical latent structure for variational conversation modeling"
    - "deep contextualized word representations"
    - "mojitalk generating emotional responses at scale"
    - "memory augmented neural networks with wormhole connections"
    - "augmenting end to end dialog systems with commonsense knowledge"
    - "pointing the unknown words"
    - "a persona based neural conversation model"

Connected through hyperedge HE-2 (induced by source paper "learning numeracy binary arithmetic with neural turing machines"):
  with members:
    - "implementing neural turing machines"
    - "lie access neural turing machine"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "learning simple algorithms from examples"
    - "deep learning in neural networks"
    - "neural gpus learn algorithms"
    - "rmsprop and equilibrated adaptive learning rates for non convex optimization"
    - "neural turing machines"

Connected through hyperedge HE-3 (induced by source paper "end to end answer chunk extraction and ranking for reading comprehension"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "attentive pooling networks"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "a parallel hierarchical model for machine comprehension on sparse data"
    - "attention based convolutional neural network for machine comprehension"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"
    - "iterative alternating neural attention for machine reading"

Connected through hyperedge HE-4 (induced by source paper "learning to update auto associative memory in recurrent neural networks for improving sequence memorization"):
  with members:
    - "end to end answer chunk extraction and ranking for reading comprehension"
    - "deep reinforcement learning for dialogue generation"
    - "squad 100 000 questions for machine comprehension of text"
    - "recurrent highway networks"
    - "dense associative memory for pattern recognition"
    - "neural architecture search with reinforcement learning"
    - "sequence to sequence rnns for text summarization"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-5 (induced by source paper "dynamic neural turing machine with soft and hard addressing schemes"):
  with members:
    - "lie access neural turing machine"
    - "noisy activation functions"
    - "recurrent dropout without memory loss"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "dynamic memory networks for visual and textual question answering"
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "recurrent batch normalization"
    - "a simple way to initialize recurrent networks of rectified linear units"

Connected through hyperedge HE-6 (induced by source paper "lie access neural turing machine"):
  with members:
    - "associative long short term memory"
    - "key value memory networks for directly reading documents"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "end to end memory networks"
    - "neural random access machines"
    - "learning to transduce with unbounded memory"
    - "grid long short term memory"
    - "inferring algorithmic patterns with stack augmented recurrent nets"

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)