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: "memory augmented neural networks with wormhole connections"
  Abstract: Recent empirical results on long-term dependency tasks have shown that neural networks augmented with an external memory can learn the long-term dependency tasks more easily and achieve better generalization than vanilla recurrent neural networks (RNN). We suggest that memory augmented neural networks can reduce the effects of vanishing gradients by creating shortcut (or wormhole) connections. Based on this observation, we propose a novel memory augmented neural network model called TARDIS (Temporal Automatic Relation Discovery in Sequences). The controller of TARDIS can store a selective set of embeddings of its own previous hidden states into an external memory and revisit them as and when needed. For TARDIS, memory acts as a storage for wormhole connections to the past to propagate the gradients more effectively and it helps to learn the temporal dependencies. The memory structure of TARDIS has similarities to both Neural Turing Machines (NTM) and Dynamic Neural Turing Machines (D-NTM), but both read and write operations of TARDIS are simpler and more efficient. We use discrete addressing for read/write operations which helps to substantially to reduce the vanishing gradient problem with very long sequences. Read and write operations in TARDIS are tied with a heuristic once the memory becomes full, and this makes the learning problem simpler when compared to NTM or D-NTM type of architectures. We provide a detailed analysis on the gradient propagation in general for MANNs. We evaluate our models on different long-term dependency tasks and report competitive results in all of them.

Connected through hyperedge HE-1 (induced by source paper "improving the gating mechanism of recurrent neural networks"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "transformer xl attentive language models beyond a fixed length context"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "fast parametric learning with activation memorization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "towards binary valued gates for robust lstm training"
    - "optimizing agent behavior over long time scales by transporting value"
    - "trellis networks for sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "ordered memory"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "cooperative learning of disjoint syntax and semantics"
    - "listops a diagnostic dataset for latent tree learning"
    - "improved sentence modeling using suffix bidirectional lstm"
    - "systematic generalization what is required and can it be learned"
    - "deep contextualized word representations"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "the importance of being recurrent for modeling hierarchical structure"

Connected through hyperedge HE-3 (induced by source paper "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"):
  with members:
    - "learning to encode text as human readable summaries using generative adversarial networks"
    - "selective encoding for abstractive sentence summarization"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "unsupervised neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "differentiable scheduled sampling for credit assignment"
    - "controllable abstractive summarization"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "improving long handwritten text line recognition with convolutional multi way associative memory"):
  with members:
    - "learning to remember more with less memorization"
    - "dual control memory augmented neural networks for treatment recommendations"
    - "memory augmented neural networks for predictive process analytics"
    - "robust and scalable differentiable neural computer for question answering"
    - "rosetta large scale system for text detection and recognition in images"
    - "dual memory neural computer for asynchronous two view sequential learning"
    - "handwritten digit string recognition by combination of residual network and rnn ctc"
    - "implicit language model in lstm for ocr"

Connected through hyperedge HE-5 (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"
    - "augmenting end to end dialog systems with commonsense knowledge"
    - "pointing the unknown words"
    - "a persona based neural conversation model"
    - "dynamic neural turing machine with soft and hard addressing schemes"

Connected through hyperedge HE-6 (induced by source paper "towards non saturating recurrent units for modelling long term dependencies"):
  with members:
    - "can recurrent neural networks warp time"
    - "gated orthogonal recurrent units on learning to forget"
    - "the statistical recurrent unit"
    - "rotational unit of memory"
    - "regularizing and optimizing lstm language models"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "noisy activation functions"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "armin towards a more efficient and light weight recurrent memory network"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "towards binary valued gates for robust lstm training"
    - "sparse attentive backtracking temporal creditassignment through reminding"
    - "fast slow recurrent neural networks"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "associative long short term memory"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-8 (induced by source paper "understanding memory modules on learning simple algorithms"):
  with members:
    - "understanding recurrent neural state using memory signatures"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "state gradients for rnn memory analysis"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "neural semantic encoders"

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)