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: "a neural compositional paradigm for image captioning"
  Abstract: Mainstream captioning models often follow a sequential structure to generate captions, leading to issues such as introduction of irrelevant semantics, lack of diversity in the generated captions, and inadequate generalization performance. In this paper, we present an alternative paradigm for image captioning, which factorizes the captioning procedure into two stages: (1) extracting an explicit semantic representation from the given image; and (2) constructing the caption based on a recursive compositional procedure in a bottom-up manner. Compared to conventional ones, our paradigm better preserves the semantic content through an explicit factorization of semantics and syntax. By using the compositional generation procedure, caption construction follows a recursive structure, which naturally fits the properties of human language. Moreover, the proposed compositional procedure requires less data to train, generalizes better, and yields more diverse captions.

Connected through hyperedge HE-1 (induced by source paper "relationship embedded representation learning for grounding referring expressions"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "cross modal relationship inference for grounding referring expressions"
    - "murel multimodal relational reasoning for visual question answering"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "graph r cnn for scene graph generation"
    - "contrast oriented deep neural networks for salient object detection"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-2 (induced by source paper "clara clinical report auto completion"):
  with members:
    - "clinically accurate chest x ray report generation"
    - "knowledge driven encode retrieve paraphrase for medical image report generation"
    - "neural baby talk"
    - "personalized language model for query auto completion"
    - "on the automatic generation of medical imaging reports"
    - "hybrid retrieval generation reinforced agent for medical image report generation"
    - "producing radiologist quality reports for interpretable artificial intelligence"
    - "learning to summarize radiology findings"

Connected through hyperedge HE-3 (induced by source paper "a case study on combining asr and visual features for generating instructional video captions"):
  with members:
    - "learning video representations using contrastive bidirectional transformer"
    - "multimodal abstractive summarization for how2 videos"
    - "videobert a joint model for video and language representation learning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "learning to localize and align fine grained actions to sparse instructions"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "audio visual event localization in unconstrained videos"
    - "end to end dense video captioning with masked transformer"

Connected through hyperedge HE-4 (induced by source paper "non autoregressive video captioning with iterative refinement"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "temporal deformable convolutional encoder decoder networks for video captioning"
    - "mask predict parallel decoding of conditional masked language models"
    - "memory attended recurrent network for video captioning"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reconstruction network for video captioning"

Connected through hyperedge HE-5 (induced by source paper "cooperative image captioning"):
  with members:
    - "binary image selection bison interpretable evaluation of visual grounding"
    - "evaluating text to image matching using binary image selection bison"
    - "learning to describe differences between pairs of similar images"
    - "how agents see things on visual representations in an emergent language game"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "natural language does not emerge naturally in multi agent dialog"
    - "speaking the same language matching machine to human captions by adversarial training"

Connected through hyperedge HE-6 (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"
    - "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"
    - "audio visual event localization in unconstrained videos"

Connected through hyperedge HE-7 (induced by source paper "fast image caption generation with position alignment"):
  with members:
    - "imitation learning for non autoregressive neural machine translation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "non autoregressive neural machine translation"
    - "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-8 (induced by source paper "masked non autoregressive image captioning"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "non autoregressive neural machine translation"
    - "convolutional 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"

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)