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: "knowledge aided consistency for weakly supervised phrase grounding"
  Abstract: Given a natural language query, a phrase grounding system aims to localize mentioned objects in an image. In weakly supervised scenario, mapping between image regions (i.e., proposals) and language is not available in the training set. Previous methods address this deficiency by training a grounding system via learning to reconstruct language information contained in input queries from predicted proposals. However, the optimization is solely guided by the reconstruction loss from the language modality, and ignores rich visual information contained in proposals and useful cues from external knowledge. In this paper, we explore the consistency contained in both visual and language modalities, and leverage complementary external knowledge to facilitate weakly supervised grounding. We propose a novel Knowledge Aided Consistency Network (KAC Net) which is optimized by reconstructing input query and proposal's information. To leverage complementary knowledge contained in the visual features, we introduce a Knowledge Based Pooling (KBP) gate to focus on query-related proposals. Experiments show that KAC Net provides a significant improvement on two popular datasets.

Connected through hyperedge HE-1 (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"
    - "multi level multimodal common semantic space for image phrase grounding"
    - "using syntax to ground referring expressions in natural images"
    - "stacked cross attention for image text matching"
    - "deep sets"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "zero shot grounding of objects from natural language queries"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "neural baby talk"
    - "graph r cnn for scene graph generation"
    - "yolov3 an incremental improvement"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "query guided regression network with context policy for phrase grounding"

Connected through hyperedge HE-3 (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"
    - "real time referring expression comprehension by single stage grounding network"
    - "mattnet modular attention network for referring expression comprehension"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"
    - "reconstruction network for video captioning"

Connected through hyperedge HE-4 (induced by source paper "harvesting information from captions for weakly supervised semantic segmentation"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "saliency guided attention network for image sentence matching"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "stacked cross attention for image text matching"
    - "self erasing network for integral object attention"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "dual path convolutional image text embedding with instance loss"
    - "learning semantic concepts and order for image and sentence matching"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-6 (induced by source paper "knowledge guided pairwise reconstruction network for weakly supervised referring expression grounding"):
  with members:
    - "referring relationships"
    - "real time referring expression comprehension by single stage grounding network"
    - "mattnet modular attention network for referring expression comprehension"
    - "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-7 (induced by source paper "phrase localization without paired training examples"):
  with members:
    - "unsupervised textual grounding linking words to image concepts"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "query guided regression network with context policy for phrase grounding"
    - "conditional image text embedding networks"
    - "discriminative learning of open vocabulary object retrieval and localization by negative phrase augmentation"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-8 (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"
    - "real time referring expression comprehension by single stage grounding network"
    - "mattnet modular attention network for referring expression comprehension"
    - "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)