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: "modeling context between objects for referring expression understanding"
  Abstract: Referring expressions usually describe an object using properties of the object and relationships of the object with other objects. We propose a technique that integrates context between objects to understand referring expressions. Our approach uses an LSTM to learn the probability of a referring expression, with input features from a region and a context region. The context regions are discovered using multiple-instance learning (MIL) since annotations for context objects are generally not available for training. We utilize max-margin based MIL objective functions for training the LSTM. Experiments on the Google RefExp and UNC RefExp datasets show that modeling context between objects provides better performance than modeling only object properties. We also qualitatively show that our technique can ground a referring expression to its referred region along with the supporting context region.

Connected through hyperedge HE-1 (induced by source paper "relationship embedded representation learning for grounding referring expressions"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "cross modal relationship inference for grounding referring expressions"
    - "murel multimodal relational reasoning for visual question answering"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "a neural compositional paradigm for image captioning"
    - "graph r cnn for scene graph generation"
    - "contrast oriented deep neural networks for salient object detection"

Connected through hyperedge HE-2 (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-3 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences"):
  with members:
    - "dynamic graph attention for referring expression comprehension"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "nodis neural ordinary differential scene understanding"):
  with members:
    - "referring relationships"
    - "graph r cnn for scene graph generation"
    - "auto encoding scene graphs for image captioning"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "explainable and explicit visual reasoning over scene graphs"
    - "combining deep learning and qualitative spatial reasoning to learn complex structures from sparse examples with noise"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-5 (induced by source paper "cops ref a new dataset and task on compositional referring expression comprehension"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "visual semantic reasoning for image text matching"
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "binary image selection bison interpretable evaluation of visual grounding"
    - "cross modal self attention network for referring image segmentation"
    - "neural sequential phrase grounding seqground"
    - "cross modal relationship inference for grounding referring expressions"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"

Connected through hyperedge HE-6 (induced by source paper "learning object placements for relational instructions by hallucinating scene representations"):
  with members:
    - "self supervised 3d shape and viewpoint estimation from single images for robotics"
    - "fast scnn fast semantic segmentation network"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "referring relationships"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "context aware synthesis and placement of object instances"
    - "mattnet modular attention network for referring expression comprehension"
    - "a multimodal classifier generative adversarial network for carry and place tasks from ambiguous language instructions"

Connected through hyperedge HE-7 (induced by source paper "modularized textual grounding for counterfactual resilience"):
  with members:
    - "weakly supervised attention learning for textual phrases grounding"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "w talc weakly supervised temporal activity localization and classification"
    - "mattnet modular attention network for referring expression comprehension"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "generating counterfactual explanations with natural language"
    - "exploring the limits of weakly supervised pretraining"
    - "unsupervised textual grounding linking words to image concepts"

Connected through hyperedge HE-8 (induced by source paper "differentiable scene graphs"):
  with members:
    - "spatio temporal action graph networks"
    - "dynamic graph attention for referring expression comprehension"
    - "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"

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)