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: "semantic image retrieval via active grounding of visual situations"
  Abstract: We describe a novel architecture for semantic image retrieval---in particular, retrieval of instances of visual situations. Visual situations are concepts such as "a boxing match," "walking the dog," "a crowd waiting for a bus," or "a game of ping-pong," whose instantiations in images are linked more by their common spatial and semantic structure than by low-level visual similarity. Given a query situation description, our architecture---called Situate---learns models capturing the visual features of expected objects as well the expected spatial configuration of relationships among objects. Given a new image, Situate uses these models in an attempt to ground (i.e., to create a bounding box locating) each expected component of the situation in the image via an active search procedure. Situate uses the resulting grounding to compute a score indicating the degree to which the new image is judged to contain an instance of the situation. Such scores can be used to rank images in a collection as part of a retrieval system. In the preliminary study described here, we demonstrate the promise of this system by comparing Situate's performance with that of two baseline methods, as well as with a related semantic image-retrieval system based on "scene graphs."

Connected through hyperedge HE-1 (induced by source paper "object centric image generation from layouts"):
  with members:
    - "interactive image generation using scene graphs"
    - "scene graph generation with external knowledge and image reconstruction"
    - "object driven text to image synthesis via adversarial training"
    - "image synthesis from reconfigurable layout and style"
    - "pastegan a semi parametric method to generate image from scene graph"
    - "locality and compositionality in zero shot learning"
    - "semantic image synthesis with spatially adaptive normalization"
    - "semantics disentangling for text to image generation"

Connected through hyperedge HE-2 (induced by source paper "revisiting visual grounding"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "differentiable scene graphs"
    - "referring relationships"
    - "using syntax to ground referring expressions in natural images"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "visual referring expression recognition what do systems actually learn"
    - "open vocabulary phrase detection"
    - "grounding referring expressions in images by variational context"

Connected through hyperedge HE-3 (induced by source paper "specifying object attributes and relations in interactive scene generation"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "image generation from scene graphs"
    - "image generation from layout"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-4 (induced by source paper "semantic image retrieval by uniting deep neural networks and cognitive architectures"):
  with members:
    - "deformable convolutional networks"
    - "iqa visual question answering in interactive environments"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "r fcn object detection via region based fully convolutional networks"
    - "you only look once unified real time object detection"
    - "microsoft coco common objects in context"
    - "deep visual semantic alignments for generating image descriptions"

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)