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: "trespassing the boundaries labeling temporal bounds for object interactions in egocentric video"
  Abstract: Manual annotations of temporal bounds for object interactions (i.e. start and end times) are typical training input to recognition, localization and detection algorithms. For three publicly available egocentric datasets, we uncover inconsistencies in ground truth temporal bounds within and across annotators and datasets. We systematically assess the robustness of state-of-the-art approaches to changes in labeled temporal bounds, for object interaction recognition. As boundaries are trespassed, a drop of up to 10% is observed for both Improved Dense Trajectories and Two-Stream Convolutional Neural Network. #R##N#We demonstrate that such disagreement stems from a limited understanding of the distinct phases of an action, and propose annotating based on the Rubicon Boundaries, inspired by a similarly named cognitive model, for consistent temporal bounds of object interactions. Evaluated on a public dataset, we report a 4% increase in overall accuracy, and an increase in accuracy for 55% of classes when Rubicon Boundaries are used for temporal annotations.

Connected through hyperedge HE-1 (induced by source paper "epic fusion audio visual temporal binding for egocentric action recognition"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "a better baseline for ava"
    - "learnable pins cross modal embeddings for person identity"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "2 5d visual sound"

Connected through hyperedge HE-2 (induced by source paper "action recognition from single timestamp supervision in untrimmed videos"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "a flexible model for training action localization with varying levels of supervision"
    - "w talc weakly supervised temporal activity localization and classification"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-3 (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"
    - "action completion a temporal model for moment detection"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "diagnosing error in temporal action detectors"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "guess where actor supervision for spatiotemporal action localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "activitynet challenge 2017 summary"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "single shot temporal action detection"
    - "what actions are needed for understanding human actions in videos"

Connected through hyperedge HE-5 (induced by source paper "scaling egocentric vision the epic kitchens dataset"):
  with members:
    - "action completion a temporal model for moment detection"
    - "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"
    - "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"
    - "action completion a temporal model for moment detection"
    - "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"

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)