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: "rethinking the form of latent states in image captioning"
  Abstract: RNNs and their variants have been widely adopted for image captioning. In RNNs, the production of a caption is driven by a sequence of latent states. Existing captioning models usually represent latent states as vectors, taking this practice for granted. We rethink this choice and study an alternative formulation, namely using two-dimensional maps to encode latent states. This is motivated by the curiosity about a question: how the spatial structures in the latent states affect the resultant captions? Our study on MSCOCO and Flickr30k leads to two significant observations. First, the formulation with 2D states is generally more effective in captioning, consistently achieving higher performance with comparable parameter sizes. Second, 2D states preserve spatial locality. Taking advantage of this, we visually reveal the internal dynamics in the process of caption generation, as well as the connections between input visual domain and output linguistic domain.

Connected through hyperedge HE-1 (induced by source paper "exploring overall contextual information for image captioning in human like cognitive style"):
  with members:
    - "knowledge embedded representation learning for fine grained image recognition"
    - "neural baby talk"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "learning to parse and translate improves neural machine translation"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "convolutional image captioning"
    - "skeleton key image captioning by skeleton attribute decomposition"
    - "an analysis of scale invariance in object detection snip"

Connected through hyperedge HE-2 (induced by source paper "recursive visual sound separation using minus plus net"):
  with members:
    - "feature intertwiner for object detection"
    - "the conversation deep audio visual speech enhancement"
    - "learning to localize sound source in visual scenes"
    - "a neural compositional paradigm for image captioning"
    - "move forward and tell a progressive generator of video descriptions"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "neural network encapsulation"

Connected through hyperedge HE-3 (induced by source paper "image captioning based on deep learning methods a survey"):
  with members:
    - "cnn cnn convolutional decoders for image captioning"
    - "learning to guide decoding for image captioning"
    - "exploring visual relationship for image captioning"
    - "mat a multimodal attentive translator for image captioning"
    - "convolutional image captioning"
    - "review networks for caption generation"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"

Connected through hyperedge HE-4 (induced by source paper "a neural compositional paradigm for image captioning"):
  with members:
    - "teaching machines to describe images via natural language feedback"
    - "contrastive learning for image captioning"
    - "skeleton key image captioning by skeleton attribute decomposition"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "boosting image captioning with attributes"

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.CV(Computer Vision and Pattern Recognition)