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: "actor and action video segmentation from a sentence"
  Abstract: This paper strives for pixel-level segmentation of actors and their actions in video content. Different from existing works, which all learn to segment from a fixed vocabulary of actor and action pairs, we infer the segmentation from a natural language input sentence. This allows to distinguish between fine-grained actors in the same super-category, identify actor and action instances, and segment pairs that are outside of the actor and action vocabulary. We propose a fully-convolutional model for pixel-level actor and action segmentation using an encoder-decoder architecture optimized for video. To show the potential of actor and action video segmentation from a sentence, we extend two popular actor and action datasets with more than 7,500 natural language descriptions. Experiments demonstrate the quality of the sentence-guided segmentations, the generalization ability of our model, and its advantage for traditional actor and action segmentation compared to the state-of-the-art.

Connected through hyperedge HE-1 (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-2 (induced by source paper "proposal free temporal moment localization of a natural language query in video using guided attention"):
  with members:
    - "tripping through time efficient localization of activities in videos"
    - "excl extractive clip localization using natural language descriptions"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "diagnosing error in temporal action detectors"
    - "mac mining activity concepts for language based temporal localization"
    - "multilevel language and vision integration for text to clip retrieval"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "to find where you talk temporal sentence localization in video with attention based location regression"

Connected through hyperedge HE-3 (induced by source paper "hallucinet ing spatiotemporal representations using 2d cnn"):
  with members:
    - "design light weight 3d convolutional networks for video recognition temporal residual fully separable block and fast algorithm"
    - "what and how well you performed a multitask learning approach to action quality assessment"
    - "efficient video classification using fewer frames"
    - "temporal sequence distillation towards few frame action recognition in videos"
    - "motion feature network fixed motion filter for action recognition"
    - "rgb d based action recognition with light weight 3d convolutional networks"
    - "the pros and cons rank aware temporal attention for skill determination in long videos"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-4 (induced by source paper "self monitoring navigation agent via auxiliary progress estimation"):
  with members:
    - "neural baby talk"
    - "talk the walk navigating new york city through grounded dialogue"
    - "gibson env real world perception for embodied agents"
    - "speaker follower models for vision and language navigation"
    - "guided feature transformation gft a neural language grounding module for embodied agents"
    - "on evaluation of embodied navigation agents"
    - "end to end dense video captioning with masked transformer"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"

Connected through hyperedge HE-5 (induced by source paper "an efficient 3d cnn for action object segmentation in video"):
  with members:
    - "videocapsulenet a simplified network for action detection"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "learning to segment moving objects"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "action tubelet detector for spatio temporal action localization"

Connected through hyperedge HE-6 (induced by source paper "semantic conditioned dynamic modulation for temporal sentence grounding in videos"):
  with members:
    - "sentence specified dynamic video thumbnail generation"
    - "spatio temporal video re localization by warp lstm"
    - "weakly supervised spatio temporally grounding natural sentence in video"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "mac mining activity concepts for language based temporal localization"
    - "video re localization"
    - "multi granularity generator for temporal action proposal"
    - "multilevel language and vision integration for text to clip retrieval"

Connected through hyperedge HE-7 (induced by source paper "referring to objects in videos using spatio temporal identifying descriptions"):
  with members:
    - "mattnet modular attention network for referring expression comprehension"
    - "visual referring expression recognition what do systems actually learn"
    - "tvqa localized compositional video question answering"
    - "flow guided feature aggregation for video object detection"
    - "obj2text generating visually descriptive language from object layouts"
    - "dense captioning events in videos"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "tall temporal activity localization via language query"

Connected through hyperedge HE-8 (induced by source paper "embodied vision and language navigation with dynamic convolutional filters"):
  with members:
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "gibson env real world perception for embodied agents"
    - "speaker follower models for vision and language navigation"
    - "on evaluation of embodied navigation agents"
    - "neural modular control for embodied question answering"
    - "attention is all you need"

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)