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: "phi lstm a phrase based hierarchical lstm model for image captioning"
  Abstract: A picture is worth a thousand words. Not until recently, however, we noticed some success stories in understanding of visual scenes: a model that is able to detect/name objects, describe their attributes, and recognize their relationships/interactions. In this paper, we propose a phrase-based hierarchical Long Short-Term Memory (phi-LSTM) model to generate image description. The proposed model encodes sentence as a sequence of combination of phrases and words, instead of a sequence of words alone as in those conventional solutions. The two levels of this model are dedicated to i) learn to generate image relevant noun phrases, and ii) produce appropriate image description from the phrases and other words in the corpus. Adopting a convolutional neural network to learn image features and the LSTM to learn the word sequence in a sentence, the proposed model has shown better or competitive results in comparison to the state-of-the-art models on Flickr8k and Flickr30k datasets.

Connected through hyperedge HE-1 (induced by source paper "comic toward a compact image captioning model with attention"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "contrastive learning for image captioning"
    - "massive exploration of neural machine translation architectures"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-2 (induced by source paper "gated hierarchical attention for image captioning"):
  with members:
    - "cnn cnn convolutional decoders for image captioning"
    - "dual recurrent attention units for visual question answering"
    - "learning to evaluate image captioning"
    - "learn to pay attention"
    - "improved image captioning via policy gradient optimization of spider"
    - "attention is all you need"
    - "convolutional image captioning"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"

Connected through hyperedge HE-3 (induced by source paper "semstyle learning to generate stylised image captions using unaligned text"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "controlling linguistic style aspects in neural language generation"
    - "areas of attention for image captioning"
    - "spice semantic propositional image caption evaluation"
    - "boosting image captioning with attributes"
    - "improving lstm based video description with linguistic knowledge mined from text"
    - "captioning images with diverse objects"

Connected through hyperedge HE-4 (induced by source paper "cnn cnn convolutional decoders for image captioning"):
  with members:
    - "skeleton key image captioning by skeleton attribute decomposition"
    - "convolutional sequence to sequence learning"
    - "semantic compositional networks for visual captioning"
    - "image captioning with semantic attention"
    - "language modeling with gated convolutional networks"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "boosting image captioning with attributes"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-5 (induced by source paper "coarse to fine a rnn based hierarchical attention model for vehicle re identification"):
  with members:
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "ssd single shot multibox detector"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "a large scale car dataset for fine grained categorization and verification"
    - "what value do explicit high level concepts have in vision to language problems"
    - "cross convolutional layer pooling for image recognition"
    - "deep feature learning with relative distance comparison for person re identification"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-6 (induced by source paper "skeleton key image captioning by skeleton attribute decomposition"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"
    - "language models for image captioning the quirks and what works"
    - "what value do explicit high level concepts have in vision to language problems"
    - "deep residual learning for image recognition"
    - "phrase based image captioning"
    - "exploring nearest neighbor approaches 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.CL(Computation and Language)