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: "encode review and decode reviewer module for caption generation"
  Abstract: We propose a novel module, the reviewer module, to improve the encoder-decoder learning framework. The reviewer module is generic, and can be plugged into an existing encoder-decoder model. The reviewer module performs a number of review steps with attention mechanism on the encoder hidden states, and outputs a fact vector after each review step; the fact vectors are used as the input of the attention mechanism in the decoder. We show that the conventional encoder-decoders are a special case of our framework. Empirically, we show that our framework can improve over state-of-the-art encoder-decoder systems on the tasks of image captioning and source code captioning.

Connected through hyperedge HE-1 (induced by source paper "mrrc multiple role representation crossover interpretation for image captioning with r cnn feature distribution composition fdc"):
  with members:
    - "learning to guide decoding for image captioning"
    - "neural baby talk"
    - "coco cn for cross lingual image tagging captioning and retrieval"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "attentive tensor product learning"
    - "pragmatically informative image captioning with character level reference"
    - "adversarial semantic alignment for improved image captions"
    - "entity aware image caption generation"

Connected through hyperedge HE-2 (induced by source paper "crur coupled recurrent unit for unification conceptualization and context capture for language representation a generalization of bi directional lstm"):
  with members:
    - "learning to guide decoding for image captioning"
    - "neural baby talk"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "pragmatically informative image captioning with character level reference"
    - "adversarial semantic alignment for improved image captions"
    - "entity aware image caption generation"
    - "joint image captioning and question answering"
    - "improved image captioning via policy gradient optimization of spider"

Connected through hyperedge HE-3 (induced by source paper "better understanding hierarchical visual relationship for image caption"):
  with members:
    - "selective attention based graph convolutional networks for aspect level sentiment classification"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "generating wikipedia by summarizing long sequences"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "person re identification with deep similarity guided graph neural network"
    - "scaling neural machine translation"
    - "a comprehensive survey of deep learning for image captioning"
    - "videos as space time region graphs"

Connected through hyperedge HE-4 (induced by source paper "taking a hint leveraging explanations to make vision and language models more grounded"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "women also snowboard overcoming bias in captioning models"
    - "neural baby talk"
    - "choose your neuron incorporating domain knowledge through neuron importance"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "axiomatic attribution for deep networks"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-5 (induced by source paper "image captioning with weakly supervised attention penalty"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "neural baby talk"
    - "attention is all you need"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "attention correctness in neural image captioning"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "automatic description generation from images a survey of models datasets and evaluation measures"

Connected through hyperedge HE-6 (induced by source paper "look and modify modification networks for image captioning"):
  with members:
    - "neural baby talk"
    - "universal sentence encoder"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "boosting image captioning with attributes"

Connected through hyperedge HE-7 (induced by source paper "token level and sequence level loss smoothing for rnn language models"):
  with members:
    - "data augmentation for low resource neural machine translation"
    - "an analysis of deep neural network models for practical applications"
    - "state of the art speech recognition with sequence to sequence models"
    - "data noising as smoothing in neural network language models"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "areas of attention for image captioning"
    - "image captioning with semantic attention"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-8 (induced by source paper "neural baby talk"):
  with members:
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "comprehension guided referring expressions"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "mask r cnn"
    - "modeling relationships in referential expressions with compositional modular networks"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

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)