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 pooling approach to modelling spatial relations for image retrieval and annotation"
  Abstract: Over the last two decades we have witnessed strong progress on modeling visual object classes, scenes and attributes that have significantly contributed to automated image understanding. On the other hand, surprisingly little progress has been made on incorporating a spatial representation and reasoning in the inference process. In this work, we propose a pooling interpretation of spatial relations and show how it improves image retrieval and annotations tasks involving spatial language. Due to the complexity of the spatial language, we argue for a learning-based approach that acquires a representation of spatial relations by learning parameters of the pooling operator. We show improvements on previous work on two datasets and two different tasks as well as provide additional insights on a new dataset with an explicit focus on spatial relations.

Connected through hyperedge HE-1 (induced by source paper "learning to generate unambiguous spatial referring expressions for real world environments"):
  with members:
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "using syntax to ground referring expressions in natural images"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "identifying spatial relations in images using convolutional neural networks"
    - "grounding spatio semantic referring expressions for human robot interaction"
    - "a joint speaker listener reinforcer model for referring expressions"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "generation and comprehension of unambiguous object descriptions"

Connected through hyperedge HE-2 (induced by source paper "a simple neural network module for relational reasoning"):
  with members:
    - "deep sets"
    - "inferring and executing programs for visual reasoning"
    - "an analysis of visual question answering algorithms"
    - "learning to reason end to end module networks for visual question answering"
    - "discovering objects and their relations from entangled scene representations"
    - "gated graph sequence neural networks"
    - "ask your neurons a deep learning approach to visual question answering"
    - "chains of reasoning over entities relations and text using recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "acquiring common sense spatial knowledge through implicit spatial templates"):
  with members:
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "don t just listen use your imagination leveraging visual common sense for non visual tasks"
    - "draw a recurrent neural network for image generation"
    - "fully convolutional networks for semantic segmentation"
    - "zero shot learning through cross modal transfer"

Connected through hyperedge HE-4 (induced by source paper "ask your neurons a deep learning approach to visual question answering"):
  with members:
    - "hadamard product for low rank bilinear pooling"
    - "a focused dynamic attention model for visual question answering"
    - "hierarchical co attention for visual question answering"
    - "dualnet domain invariant network for visual question answering"
    - "segmentation from natural language expressions"
    - "tutorial on answering questions about images with deep learning"
    - "grounding of textual phrases in images by reconstruction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"

Connected through hyperedge HE-5 (induced by source paper "resolving language and vision ambiguities together joint segmentation prepositional attachment resolution in captioned scenes"):
  with members:
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
    - "do you see what i mean visual resolution of linguistic ambiguities"
    - "vqa visual question answering"
    - "visual madlibs fill in the blank image generation and question answering"
    - "ask your neurons a neural based approach to answering questions about images"
    - "cider consensus based image description evaluation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"

Connected through hyperedge HE-6 (induced by source paper "learning like a child fast novel visual concept learning from sentence descriptions of images"):
  with members:
    - "multimodal convolutional neural networks for matching image and sentence"
    - "vqa visual question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "exploring nearest neighbor approaches for image captioning"
    - "microsoft coco captions data collection and evaluation server"
    - "are you talking to a machine dataset and methods for multilingual image question answering"
    - "simple image description generator via a linear phrase based approach"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "hard to cheat a turing test based on answering questions about images"):
  with members:
    - "long term recurrent convolutional networks for visual recognition and description"
    - "deep fragment embeddings for bidirectional image sentence mapping"
    - "towards a visual turing challenge"
    - "a multi world approach to question answering about real world scenes based on uncertain input"
    - "deep visual semantic alignments for generating image descriptions"

Connected through hyperedge HE-8 (induced by source paper "contextual media retrieval using natural language queries"):
  with members:
    - "towards a visual turing challenge"
    - "a multi world approach to question answering about real world scenes based on uncertain input"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"
    - "learning dependency based compositional semantics"

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)