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: "multi level multimodal common semantic space for image phrase grounding"
  Abstract: We address the problem of phrase grounding by learning a multi-level common semantic space shared by the textual and visual modalities. This common space is instantiated at multiple layers of a Deep Convolutional Neural Network by exploiting its feature maps, as well as contextualized word-level and sentence-level embeddings extracted from a character-based language model. Following a dedicated non-linear mapping for visual features at each level, word, and sentence embeddings, we obtain a common space in which comparisons between the target text and the visual content at any semantic level can be performed simply with cosine similarity. We guide the model by a multi-level multimodal attention mechanism which outputs attended visual features at different semantic levels. The best level is chosen to be compared with text content for maximizing the pertinence scores of image-sentence pairs of the ground truth. Experiments conducted on three publicly available benchmarks show significant performance gains (20%-60% relative) over the state-of-the-art in phrase localization and set a new performance record on those datasets. We also provide a detailed ablation study to show the contribution of each element of our approach.

Connected through hyperedge HE-1 (induced by source paper "multimodal data fusion based on the global workspace theory"):
  with members:
    - "automatic detection of protective behavior in chronic pain physical rehabilitation a recurrent neural network approach"
    - "an introductory survey on attention mechanisms in nlp problems"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "multimodal attention for neural machine translation"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "align2ground weakly supervised phrase grounding guided by image caption alignment"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "using syntax to ground referring expressions in natural images"
    - "stacked cross attention for image text matching"
    - "deep sets"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "understanding visual ads by aligning symbols and objects using co attention"
    - "learning two branch neural networks for image text matching tasks"
    - "natural language does not emerge naturally in multi agent dialog"

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)