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: "object referring in videos with language and human gaze"
  Abstract: We investigate the problem of object referring (OR) i.e. to localize a target object in a visual scene coming with a language description. Humans perceive the world more as continued video snippets than as static images, and describe objects not only by their appearance, but also by their temporal-spatial contexts and motion features. Humans also gaze at the object when they issue a referring expression. Existing works for OR mostly focus on static images only, which fall short in providing many such cues. This paper addresses OR in videos with language and human gaze. To that end, we present a new video dataset for OR, with 30, 000 objects over 5, 000 stereo video sequences annotated for their descriptions and gaze. We further propose a novel network model for OR in videos, by integrating appearance, motion, gaze, and spatial-temporal contextual information all into one network. Experimental results shows that our method effectively utilizes motion cues, human gaze, and spatial-temporal context information. Our method outperforms previous OR methods. The dataset and code will be made available.

Connected through hyperedge HE-1 (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"
    - "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 "talk2nav long range vision and language navigation with dual attention and spatial memory"):
  with members:
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "learning accurate comfortable and human like driving"
    - "learning to follow directions in street view"
    - "grounding human to vehicle advice for self driving vehicles"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "talk the walk navigating new york city through grounded dialogue"

Connected through hyperedge HE-3 (induced by source paper "talk2nav long range vision and language navigation in cities"):
  with members:
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "language conditioned graph networks for relational reasoning"
    - "grounding human to vehicle advice for self driving vehicles"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised spatio temporally grounding natural sentence in video"):
  with members:
    - "using syntax to ground referring expressions in natural images"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "motion appearance co memory networks for video question answering"
    - "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-5 (induced by source paper "talk2car taking control of your self driving car"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "mattnet modular attention network for referring expression comprehension"
    - "deep contextualized word representations"
    - "visual referring expression recognition what do systems actually learn"
    - "compositional attention networks for machine reasoning"

Connected through hyperedge HE-6 (induced by source paper "generating easy to understand referring expressions for target identifications"):
  with members:
    - "neural baby talk"
    - "mattnet modular attention network for referring expression comprehension"
    - "exploring visual relationship for image captioning"
    - "grounding referring expressions in images by variational context"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning from synthetic humans"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-7 (induced by source paper "learning accurate comfortable and human like driving"):
  with members:
    - "conditional affordance learning for driving in urban environments"
    - "learning to steer by mimicking features from heterogeneous auxiliary networks"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "driving policy transfer via modularity and abstraction"
    - "learning to drive from simulation without real world labels"
    - "learning to drive in a day"
    - "on offline evaluation of vision based driving models"
    - "event based vision meets deep learning on steering prediction for self driving cars"

Connected through hyperedge HE-8 (induced by source paper "searching for ambiguous objects in videos using relational referring expressions"):
  with members:
    - "referring to objects in videos using spatio temporal identifying descriptions"
    - "mattnet modular attention network for referring expression comprehension"
    - "modeling context between objects for referring expression understanding"
    - "generation and comprehension of unambiguous object descriptions"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "imagenet large scale visual recognition challenge"

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)