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: "rethinking diversified and discriminative proposal generation for visual grounding"
  Abstract: Visual grounding aims to localize an object in an image referred to by a textual query phrase. Various visual grounding approaches have been proposed, and the problem can be modularized into a general framework: proposal generation, multi-modal feature representation, and proposal ranking. Of these three modules, most existing approaches focus on the latter two, with the importance of proposal generation generally neglected. In this paper, we rethink the problem of what properties make a good proposal generator. We introduce the diversity and discrimination simultaneously when generating proposals, and in doing so propose Diversified and Discriminative Proposal Networks model (DDPN). Based on the proposals generated by DDPN, we propose a high performance baseline model for visual grounding and evaluate it on four benchmark datasets. Experimental results demonstrate that our model delivers significant improvements on all the tested data-sets (e.g., 18.8\% improvement on ReferItGame and 8.2\% improvement on Flickr30k Entities over the existing state-of-the-arts respectively)

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"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "bilinear graph networks for visual question answering"):
  with members:
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "deep modular co attention networks for visual question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "graph r cnn for scene graph generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "structure inference net object detection using scene level context and instance level relationships"

Connected through hyperedge HE-3 (induced by source paper "mutatt visual textual mutual guidance for referring expression comprehension"):
  with members:
    - "learning to collocate neural modules for image captioning"
    - "dynamic graph attention for referring expression comprehension"
    - "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"
    - "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-4 (induced by source paper "multimodal unified attention networks for vision and language interactions"):
  with members:
    - "shared predictive cross modal deep quantization"
    - "multimodal deep network embedding with integrated structure and attribute information"
    - "deep modular co attention networks for visual question answering"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "mattnet modular attention network for referring expression comprehension"
    - "dynamic fusion with intra and inter modality attention flow for visual question answering"
    - "compositional attention networks for machine reasoning"

Connected through hyperedge HE-5 (induced by source paper "referring expression grounding by marginalizing scene graph likelihood"):
  with members:
    - "learning to compose and reason with language tree structures for visual grounding"
    - "using syntax to ground referring expressions in natural images"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "mattnet modular attention network for referring expression comprehension"
    - "relational inductive biases deep learning and graph networks"
    - "visual question reasoning on general dependency tree"
    - "auto encoding scene graphs for image captioning"

Connected through hyperedge HE-6 (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"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "query guided regression network with context policy for phrase grounding"

Connected through hyperedge HE-7 (induced by source paper "learning to compose and reason with language tree structures for visual grounding"):
  with members:
    - "mattnet modular attention network for referring expression comprehension"
    - "inferring and executing programs for visual reasoning"
    - "grounding referring expressions in images by variational context"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "comprehension guided referring expressions"
    - "learning to reason end to end module networks for visual question answering"
    - "learning to segment every thing"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"

Connected through hyperedge HE-8 (induced by source paper "multimodal transformer with multi view visual representation for image captioning"):
  with members:
    - "bilinear attention networks"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "multi modal factorized bilinear pooling with co attention learning for visual 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)