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: "action completion a temporal model for moment detection"
  Abstract: We introduce completion moment detection for actions - the problem of locating the moment of completion, when the action's goal is confidently considered achieved. The paper proposes a joint classification-regression recurrent model that predicts completion from a given frame, and then integrates frame-level contributions to detect sequence-level completion moment. We introduce a recurrent voting node that predicts the frame's relative position of the completion moment by either classification or regression. The method is also capable of detecting incompletion. For example, the method is capable of detecting a missed ball-catch, as well as the moment at which the ball is safely caught. We test the method on 16 actions from three public datasets, covering sports as well as daily actions. Results show that when combining contributions from frames prior to the completion moment as well as frames post completion, the completion moment is detected within one second in 89% of all tested sequences.

Connected through hyperedge HE-1 (induced by source paper "temporal cycle consistency learning"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "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"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised completion moment detection using temporal attention"):
  with members:
    - "action recognition from single timestamp supervision in untrimmed videos"
    - "temporal cycle consistency learning"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "am i done predicting action progress in videos"

Connected through hyperedge HE-3 (induced by source paper "retro actions learning close by time reversing open videos"):
  with members:
    - "time reversal as self supervision"
    - "video time properties encoders and evaluation"
    - "self supervised spatiotemporal feature learning via video rotation prediction"
    - "slowfast networks for video recognition"
    - "video jigsaw unsupervised learning of spatiotemporal context for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "uncertainty aware anticipation of activities"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "action anticipation by predicting future dynamic images"
    - "when will you do what anticipating temporal occurrences of activities"
    - "bayesian prediction of future street scenes using synthetic likelihoods"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "encouraging lstms to anticipate actions very early"
    - "visual forecasting by imitating dynamics in natural sequences"
    - "red reinforced encoder decoder networks for action anticipation"

Connected through hyperedge HE-5 (induced by source paper "scaling egocentric vision the epic kitchens dataset"):
  with members:
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "from lifestyle vlogs to everyday interactions"
    - "who s better who s best pairwise deep ranking for skill determination"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "hacs human action clips and segments dataset for recognition and temporal localization"
    - "towards automatic learning of procedures from web instructional videos"
    - "trespassing the boundaries labeling temporal bounds for object interactions in egocentric video"
    - "the something something video database for learning and evaluating visual common sense"

Connected through hyperedge HE-6 (induced by source paper "am i done predicting action progress in videos"):
  with members:
    - "rsdnet learning to predict remaining surgery duration from laparoscopic videos without manual annotations"
    - "guess where actor supervision for spatiotemporal action localization"
    - "temporal recurrent networks for online action detection"
    - "what actions are needed for understanding human actions in videos"
    - "temporal action detection with structured segment networks"
    - "action tubelet detector for spatio temporal action localization"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "temporal action localization by structured maximal sums"

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)