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: "real time referring expression comprehension by single stage grounding network"
  Abstract: In this paper, we propose a novel end-to-end model, namely Single-Stage Grounding network (SSG), to localize the referent given a referring expression within an image. Different from previous multi-stage models which rely on object proposals or detected regions, our proposed model aims to comprehend a referring expression through one single stage without resorting to region proposals as well as the subsequent region-wise feature extraction. Specifically, a multimodal interactor is proposed to summarize the local region features regarding the referring expression attentively. Subsequently, a grounder is proposed to localize the referring expression within the given image directly. For further improving the localization accuracy, a guided attention mechanism is proposed to enforce the grounder to focus on the central region of the referent. Moreover, by exploiting and predicting visual attribute information, the grounder can further distinguish the referent objects within an image and thereby improve the model performance. Experiments on RefCOCO, RefCOCO+, and RefCOCOg datasets demonstrate that our proposed SSG without relying on any region proposals can achieve comparable performance with other advanced models. Furthermore, our SSG outperforms the previous models and achieves the state-of-art performance on the ReferItGame dataset. More importantly, our SSG is time efficient and can ground a referring expression in a 416*416 image from the RefCOCO dataset in 25ms (40 referents per second) on average with a Nvidia Tesla P40, accomplishing more than 9* speedups over the existing multi-stage models.

Connected through hyperedge HE-1 (induced by source paper "variational context exploiting visual and textual context for grounding referring expressions"):
  with members:
    - "you only look listen once towards fast and accurate visual grounding"
    - "mattnet modular attention network for referring expression comprehension"
    - "vip cnn visual phrase guided convolutional neural network"
    - "grounding referring expressions in images by variational context"
    - "a domain based approach to social relation recognition"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "comprehension guided referring expressions"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised spatio temporally grounding natural sentence in video"):
  with members:
    - "using syntax to ground referring expressions in natural images"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "motion appearance co memory networks for video question answering"
    - "object referring in videos with language and human gaze"
    - "mattnet modular attention network for referring expression comprehension"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"
    - "reconstruction network for video captioning"

Connected through hyperedge HE-3 (induced by source paper "knowledge guided pairwise reconstruction network for weakly supervised referring expression grounding"):
  with members:
    - "referring relationships"
    - "mattnet modular attention network for referring expression comprehension"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "query guided regression network with context policy for phrase grounding"
    - "grounding referring expressions in images by variational context"
    - "comprehension guided referring expressions"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "iqa visual question answering in interactive environments"

Connected through hyperedge HE-4 (induced by source paper "grounding tracking integration"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "zero shot grounding of objects from natural language queries"
    - "mask scoring r cnn"
    - "atom accurate tracking by overlap maximization"
    - "yolov3 an incremental improvement"
    - "mattnet modular attention network for referring expression comprehension"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-5 (induced by source paper "adaptive reconstruction network for weakly supervised referring expression grounding"):
  with members:
    - "knowledge guided pairwise reconstruction network for weakly supervised referring expression grounding"
    - "mattnet modular attention network for referring expression comprehension"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "query guided regression network with context policy for phrase grounding"
    - "grounding referring expressions in images by variational context"
    - "iqa visual question answering in interactive environments"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "embodied question answering"

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)