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: "dynamic multimodal instance segmentation guided by natural language queries"
  Abstract: We address the problem of segmenting an object given a natural language expression that describes it. Current techniques tackle this task by either (\textit{i}) directly or recursively merging linguistic and visual information in the channel dimension and then performing convolutions; or by (\textit{ii}) mapping the expression to a space in which it can be thought of as a filter, whose response is directly related to the presence of the object at a given spatial coordinate in the image, so that a convolution can be applied to look for the object. We propose a novel method that integrates these two insights in order to fully exploit the recursive nature of language. Additionally, during the upsampling process, we take advantage of the intermediate information generated when downsampling the image, so that detailed segmentations can be obtained. We compare our method against the state-of-the-art approaches in four standard datasets, in which it surpasses all previous methods in six of eight of the splits for this task.

Connected through hyperedge HE-1 (induced by source paper "multi task collaborative network for joint referring expression comprehension and segmentation"):
  with members:
    - "a real time global inference network for one stage referring expression comprehension"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "zero shot grounding of objects from natural language queries"
    - "cross modal self attention network for referring image segmentation"
    - "yolact real time instance segmentation"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "yolov3 an incremental improvement"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-2 (induced by source paper "dual convolutional lstm network for referring image segmentation"):
  with members:
    - "cross modal self attention network for referring image segmentation"
    - "mattnet modular attention network for referring expression comprehension"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "recurrent multimodal interaction for referring image segmentation"
    - "mask r cnn"
    - "segmentation from natural language expressions"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-3 (induced by source paper "scanrefer 3d object localization in rgb d scans using natural language"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "3d bird s eye view instance segmentation"
    - "dynamic graph attention for referring expression comprehension"
    - "embodied language grounding with implicit 3d visual feature representations"
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "rerere remote embodied referring expressions in real indoor environments"
    - "zero shot grounding of objects from natural language queries"
    - "cross modal self attention network for referring image segmentation"

Connected through hyperedge HE-4 (induced by source paper "a fast and accurate one stage approach to visual grounding"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "neural sequential phrase grounding seqground"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "yolov3 an incremental improvement"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "unsupervised image captioning"
    - "mattnet modular attention network for referring expression comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "unsupervised discovery of multimodal links in multi image multi sentence documents"):
  with members:
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "quantifying the visual concreteness of words and topics in multimodal datasets"
    - "exploring the limits of weakly supervised pretraining"
    - "learning transferable architectures for scalable image recognition"
    - "weakly supervised learning of visual relations"
    - "segmentation from natural language expressions"
    - "densely connected convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "clevr ref diagnosing visual reasoning with referring expressions"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "mattnet modular attention network for referring expression comprehension"
    - "visual referring expression recognition what do systems actually learn"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "comprehension guided referring expressions"

Connected through hyperedge HE-7 (induced by source paper "referring expression object segmentation with caption aware consistency"):
  with members:
    - "mattnet modular attention network for referring expression comprehension"
    - "grounding referring expressions in images by variational context"
    - "comprehension guided referring expressions"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "recurrent multimodal interaction for referring image segmentation"
    - "modeling relationships in referential expressions with compositional modular networks"
    - "a joint speaker listener reinforcer model for referring expressions"
    - "segmentation from natural language expressions"

Connected through hyperedge HE-8 (induced by source paper "cross modal self attention network for referring image segmentation"):
  with members:
    - "mattnet modular attention network for referring expression comprehension"
    - "non local neural networks"
    - "attention is all you need"
    - "recurrent multimodal interaction for referring image segmentation"
    - "modeling relationships in referential expressions with compositional modular networks"
    - "segmentation from natural language expressions"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"

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)