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: "reinforcement learning neural turing machines revised"
  Abstract: The Neural Turing Machine (NTM) is more expressive than all previously considered models because of its external memory. It can be viewed as a broader effort to use abstract external Interfaces and to learn a parametric model that interacts with them. #R##N#The capabilities of a model can be extended by providing it with proper Interfaces that interact with the world. These external Interfaces include memory, a database, a search engine, or a piece of software such as a theorem verifier. Some of these Interfaces are provided by the developers of the model. However, many important existing Interfaces, such as databases and search engines, are discrete. #R##N#We examine feasibility of learning models to interact with discrete Interfaces. We investigate the following discrete Interfaces: a memory Tape, an input Tape, and an output Tape. We use a Reinforcement Learning algorithm to train a neural network that interacts with such Interfaces to solve simple algorithmic tasks. Our Interfaces are expressive enough to make our model Turing complete.

Connected through hyperedge HE-1 (induced by source paper "reconstruct and represent video contents for captioning via reinforcement learning"):
  with members:
    - "learning to guide decoding for image captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "reinforced video captioning with entailment rewards"

Connected through hyperedge HE-2 (induced by source paper "controllable video captioning with pos sequence guidance based on gated fusion network"):
  with members:
    - "reconstruct and represent video contents for captioning via reinforcement learning"
    - "hierarchical photo scene encoder for album storytelling"
    - "less is more picking informative frames for video captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "unsupervised image captioning"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"

Connected through hyperedge HE-3 (induced by source paper "neural shuffle exchange networks sequence processing in o n log n time"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "star transformer"
    - "universal transformers"
    - "generating long sequences with sparse transformers"
    - "tensor2tensor for neural machine translation"
    - "recent advances in neural program synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"

Connected through hyperedge HE-4 (induced by source paper "recursive sketches for modular deep learning"):
  with members:
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "regularized evolution for image classifier architecture search"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "recovery guarantees for one hidden layer neural networks"
    - "learning to reason end to end module networks for visual question answering"
    - "dynamic routing between capsules"
    - "electron proton dynamics in deep learning"
    - "zero shot task generalization with multi task deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "video captioning with text based dynamic attention and step by step learning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "memory attended recurrent network for video captioning"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "reconstruction network for video captioning"
    - "end to end video captioning with multitask reinforcement learning"
    - "saliency guided video classification via adaptively weighted learning"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"

Connected through hyperedge HE-6 (induced by source paper "memory augmented recurrent neural networks can learn generalized dyck languages"):
  with members:
    - "theoretical limitations of self attention in neural sequence models"
    - "sequential neural networks as automata"
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "on evaluating the generalization of lstm models in formal languages"
    - "attention is all you need"
    - "lie access neural turing machine"
    - "learning operations on a stack with neural turing machines"

Connected through hyperedge HE-7 (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"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "rl lim reinforcement learning based locally interpretable modeling"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "regularizing black box models for improved interpretability"
    - "model agnostic supervised local explanations"
    - "explaining explanations an overview of interpretability of machine learning"
    - "learning to reweight examples for robust deep learning"
    - "on the robustness of interpretability methods"
    - "online and linear time attention by enforcing monotonic alignments"
    - "interpretable explorable approximations of black box models"

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)