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 graph attention for referring expression comprehension"
  Abstract: Referring expression comprehension aims to locate the object instance described by a natural language referring expression in an image. This task is compositional and inherently requires visual reasoning on top of the relationships among the objects in the image. Meanwhile, the visual reasoning process is guided by the linguistic structure of the referring expression. However, existing approaches treat the objects in isolation or only explore the first-order relationships between objects without being aligned with the potential complexity of the expression. Thus it is hard for them to adapt to the grounding of complex referring expressions. In this paper, we explore the problem of referring expression comprehension from the perspective of language-driven visual reasoning, and propose a dynamic graph attention network to perform multi-step reasoning by modeling both the relationships among the objects in the image and the linguistic structure of the expression. In particular, we construct a graph for the image with the nodes and edges corresponding to the objects and their relationships respectively, propose a differential analyzer to predict a language-guided visual reasoning process, and perform stepwise reasoning on top of the graph to update the compound object representation at every node. Experimental results demonstrate that the proposed method can not only significantly surpass all existing state-of-the-art algorithms across three common benchmark datasets, but also generate interpretable visual evidences for stepwisely locating the objects referred to in complex language descriptions.

Connected through hyperedge HE-1 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences"):
  with members:
    - "weakly supervised video moment retrieval from text queries"
    - "cross modal interaction networks for query based moment retrieval in videos"
    - "relation aware graph attention network for visual question answering"
    - "cross modal relationship inference for grounding referring expressions"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "object referring in videos with language and human gaze"
    - "mattnet modular attention network for referring expression comprehension"
    - "multilevel language and vision integration for text to clip retrieval"

Connected through hyperedge HE-2 (induced by source paper "mutatt visual textual mutual guidance for referring expression comprehension"):
  with members:
    - "learning to collocate neural modules for image captioning"
    - "visual semantic reasoning for image text matching"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "visual reasoning with multi hop feature modulation"
    - "identity aware textual visual matching with latent co attention"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"

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"
    - "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"
    - "neural sequential phrase grounding seqground"

Connected through hyperedge HE-4 (induced by source paper "differentiable scene graphs"):
  with members:
    - "spatio temporal action graph networks"
    - "generating easy to understand referring expressions for target identifications"
    - "vrr vg refocusing visually relevant relationships"
    - "learning canonical representations for scene graph to image generation"
    - "adaptive reconstruction network for weakly supervised referring expression grounding"
    - "specifying object attributes and relations in interactive scene generation"
    - "scene graph prediction with limited labels"
    - "triplet aware scene graph embeddings"

Connected through hyperedge HE-5 (induced by source paper "a real time cross modality correlation filtering method for referring expression comprehension"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "objects as points"
    - "cross modal relationship inference for grounding referring expressions"
    - "cornernet detecting objects as paired keypoints"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "mattnet modular attention network for referring expression comprehension"
    - "grounding referring expressions in images by variational context"

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)