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: "dynamic neural turing machine with soft and hard addressing schemes"
  Abstract: We extend neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing a trainable memory addressing scheme. This addressing scheme maintains for each memory cell two separate vectors, content and address vectors. This allows the D-NTM to learn a wide variety of location-based addressing strategies including both linear and nonlinear ones. We implement the D-NTM with both continuous, differentiable and discrete, non-differentiable read/write mechanisms. We investigate the mechanisms and effects of learning to read and write into a memory through experiments on Facebook bAbI tasks using both a feedforward and GRUcontroller. The D-NTM is evaluated on a set of Facebook bAbI tasks and shown to outperform NTM and LSTM baselines. We have done extensive analysis of our model and different variations of NTM on bAbI task. We also provide further experimental results on sequential pMNIST, Stanford Natural Language Inference, associative recall and copy tasks.

Connected through hyperedge HE-1 (induced by source paper "learning to follow directions in street view"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning visual question answering by bootstrapping hard attention"
    - "global pose estimation with an attention based recurrent network"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"

Connected through hyperedge HE-2 (induced by source paper "episodic memory reader learning what to remember for question answering from streaming data"):
  with members:
    - "multimodal dual attention memory for video story question answering"
    - "deep sets"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tvqa localized compositional video question answering"
    - "movie question answering remembering the textual cues for layered visual contents"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "beyond the memory wall a case for memory centric hpc system for deep learning"):
  with members:
    - "bit tactical exploiting ineffectual computations in convolutional neural networks which why and how"
    - "on scale out deep learning training for cloud and hpc"
    - "tbd benchmarking and analyzing deep neural network training"
    - "imagenet training in minutes"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "a read write memory network for movie story understanding"
    - "compressing dma engine leveraging activation sparsity for training deep neural networks"

Connected through hyperedge HE-4 (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-5 (induced by source paper "a review on neural turing machine"):
  with members:
    - "associative long short term memory"
    - "lstm with working memory"
    - "lie access neural turing machine"
    - "deep multi task learning with shared memory"
    - "terpret a probabilistic programming language for program induction"
    - "neural random access machines"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep learning in neural networks"

Connected through hyperedge HE-6 (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"
    - "memory augmented neural networks with wormhole connections"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "neural semantic encoders"

Connected through hyperedge HE-7 (induced by source paper "learning numeracy binary arithmetic with neural turing machines"):
  with members:
    - "implementing neural turing machines"
    - "lie access neural turing machine"
    - "learning simple algorithms from examples"
    - "deep learning in neural networks"
    - "structured memory for neural turing machines"
    - "neural gpus learn algorithms"
    - "rmsprop and equilibrated adaptive learning rates for non convex optimization"
    - "neural turing machines"

Connected through hyperedge HE-8 (induced by source paper "learning visual question answering by bootstrapping hard attention"):
  with members:
    - "hyperbolic attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "self attention with relative position representations"
    - "object based reasoning in vqa"
    - "relational inductive biases deep learning and graph networks"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"

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)