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: "using syntax to ground referring expressions in natural images"
  Abstract: We introduce GroundNet, a neural network for referring expression recognition -- the task of localizing (or grounding) in an image the object referred to by a natural language expression. Our approach to this task is the first to rely on a syntactic analysis of the input referring expression in order to inform the structure of the computation graph. Given a parse tree for an input expression, we explicitly map the syntactic constituents and relationships present in the tree to a composed graph of neural modules that defines our architecture for performing localization. This syntax-based approach aids localization of \textit{both} the target object and auxiliary supporting objects mentioned in the expression. As a result, GroundNet is more interpretable than previous methods: we can (1) determine which phrase of the referring expression points to which object in the image and (2) track how the localization of the target object is determined by the network. We study this property empirically by introducing a new set of annotations on the GoogleRef dataset to evaluate localization of supporting objects. Our experiments show that GroundNet achieves state-of-the-art accuracy in identifying supporting objects, while maintaining comparable performance in the localization of target objects.

Connected through hyperedge HE-1 (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"
    - "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"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "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-2 (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"
    - "stacked cross attention for image text matching"
    - "deep sets"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "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-3 (induced by source paper "weakly supervised spatio temporally grounding natural sentence in video"):
  with members:
    - "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"
    - "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-4 (induced by source paper "self assembling modular networks for interpretable multi hop reasoning"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "compositional questions do not necessitate multi hop reasoning"
    - "understanding dataset design choices for multi hop reasoning"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "avoiding reasoning shortcuts adversarial evaluation training and model development for multi hop qa"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-5 (induced by source paper "learning to relate from captions and bounding boxes"):
  with members:
    - "dissimilarity coefficient based weakly supervised object detection"
    - "zigzag learning for weakly supervised object detection"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "improved image captioning via policy gradient optimization of spider"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"

Connected through hyperedge HE-6 (induced by source paper "revisiting visual grounding"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "differentiable scene graphs"
    - "referring relationships"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "visual referring expression recognition what do systems actually learn"
    - "open vocabulary phrase detection"
    - "grounding referring expressions in images by variational context"
    - "semantic image retrieval via active grounding of visual situations"

Connected through hyperedge HE-7 (induced by source paper "learning from implicit information in natural language instructions for robotic manipulations"):
  with members:
    - "semantic relational object tracking"
    - "improving grounded natural language understanding through human robot dialog"
    - "mattnet modular attention network for referring expression comprehension"
    - "compositional attention networks for machine reasoning"
    - "neural modular control for embodied question answering"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"

Connected through hyperedge HE-8 (induced by source paper "natural language semantics with pictures some language vision datasets and potential uses for computational semantics"):
  with members:
    - "yolov3 an incremental improvement"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"
    - "mask r cnn"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "modeling context in referring expressions"
    - "generation and comprehension of unambiguous object descriptions"

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)