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: "segmental spatiotemporal cnns for fine grained action segmentation"
  Abstract: Joint segmentation and classification of fine-grained actions is important for applications of human-robot interaction, video surveillance, and human skill evaluation. However, despite substantial recent progress in large-scale action classification, the performance of state-of-the-art fine-grained action recognition approaches remains low. We propose a model for action segmentation which combines low-level spatiotemporal features with a high-level segmental classifier. Our spatiotemporal CNN is comprised of a spatial component that uses convolutional filters to capture information about objects and their relationships, and a temporal component that uses large 1D convolutional filters to capture information about how object relationships change across time. These features are used in tandem with a semi-Markov model that models transitions from one action to another. We introduce an efficient constrained segmental inference algorithm for this model that is orders of magnitude faster than the current approach. We highlight the effectiveness of our Segmental Spatiotemporal CNN on cooking and surgical action datasets for which we observe substantially improved performance relative to recent baseline methods.

Connected through hyperedge HE-1 (induced by source paper "a novel online action detection framework from untrimmed video streams"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "temporal action detection with structured segment networks"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "unsupervised learning for physical interaction through video prediction"
    - "online action detection"
    - "online human action detection using joint classification regression recurrent neural networks"
    - "generating videos with scene dynamics"

Connected through hyperedge HE-2 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction"):
  with members:
    - "using 3d convolutional neural networks to learn spatiotemporal features for automatic surgical gesture recognition in video"
    - "weakly supervised recognition of surgical gestures"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "end to end fine grained action segmentation and recognition using conditional random field models and discriminative sparse coding"
    - "joint surgical gesture and task classification with multi task and multimodal learning"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis"
    - "surgical data science for next generation interventions"

Connected through hyperedge HE-3 (induced by source paper "temporal segmentation of surgical sub tasks through deep learning with multiple data sources"):
  with members:
    - "weakly supervised recognition of surgical gestures"
    - "learning from a tiny dataset of manual annotations a teacher student approach for surgical phase recognition"
    - "end to end fine grained action segmentation and recognition using conditional random field models and discriminative sparse coding"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "surgical activity recognition in robot assisted radical prostatectomy using deep learning"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "recognizing surgical activities with recurrent neural networks"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"

Connected through hyperedge HE-4 (induced by source paper "automatic gesture recognition in robot assisted surgery with reinforcement learning and tree search"):
  with members:
    - "weakly supervised recognition of surgical gestures"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "end to end fine grained action segmentation and recognition using conditional random field models and discriminative sparse coding"
    - "deepphase surgical phase recognition in cataracts videos"
    - "recognizing surgical activities with recurrent neural networks"
    - "temporal convolutional networks for action segmentation and detection"
    - "temporal convolutional networks a unified approach to action segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "context aware query selection for active learning in event recognition"):
  with members:
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "temporal convolutional networks for action segmentation and detection"
    - "hierarchical subquery evaluation for active learning on a graph"
    - "action recognition by hierarchical mid level action elements"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "temporal cycle consistency learning"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "action completion a temporal model for moment detection"
    - "playing hard exploration games by watching youtube"
    - "unsupervised learning and segmentation of complex activities from video"
    - "neighbourhood consensus networks"
    - "deep unsupervised learning of visual similarities"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised action segmentation using mutual consistency"):
  with members:
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "weakly supervised energy based learning for action segmentation"
    - "large scale holistic video understanding"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "group normalization"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-8 (induced by source paper "context aware human motion prediction"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "bihmp gan bidirectional 3d human motion prediction gan"
    - "an lstm network for highway trajectory prediction"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic graph cnn for learning on point clouds"
    - "videos as space time region graphs"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "structure inference net object detection using scene level context and instance level relationships"

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)