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: "much ado about time exhaustive annotation of temporal data"
  Abstract: Large-scale annotated datasets allow AI systems to learn from and build upon the knowledge of the crowd. Many crowdsourcing techniques have been developed for collecting image annotations. These techniques often implicitly rely on the fact that a new input image takes a negligible amount of time to perceive. In contrast, we investigate and determine the most cost-effective way of obtaining high-quality multi-label annotations for temporal data such as videos. Watching even a short 30-second video clip requires a significant time investment from a crowd worker; thus, requesting multiple annotations following a single viewing is an important cost-saving strategy. But how many questions should we ask per video? We conclude that the optimal strategy is to ask as many questions as possible in a HIT (up to 52 binary questions after watching a 30-second video clip in our experiments). We demonstrate that while workers may not correctly answer all questions, the cost-benefit analysis nevertheless favors consensus from multiple such cheap-yet-imperfect iterations over more complex alternatives. When compared with a one-question-per-video baseline, our method is able to achieve a 10% improvement in recall (76.7% ours versus 66.7% baseline) at comparable precision (83.8% ours versus 83.0% baseline) in about half the annotation time (3.8 minutes ours compared to 7.1 minutes baseline). We demonstrate the effectiveness of our method by collecting multi-label annotations of 157 human activities on 1,815 videos.

Connected through hyperedge HE-1 (induced by source paper "lpat learning to predict adaptive threshold for weakly supervised temporal action localization"):
  with members:
    - "dmc net generating discriminative motion cues for fast compressed video action recognition"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "cascaded pyramid mining network for weakly supervised temporal action localization"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"

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"
    - "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-3 (induced by source paper "autoloc weakly supervised temporal action localization"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "predictive corrective networks for action detection"
    - "temporal action detection with structured segment networks"
    - "deep self taught learning for weakly supervised object localization"
    - "two phase learning for weakly supervised object localization"

Connected through hyperedge HE-4 (induced by source paper "charades ego a large scale dataset of paired third and first person videos"):
  with members:
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "first person activity forecasting with online inverse reinforcement learning"
    - "learning image representations tied to ego motion"

Connected through hyperedge HE-5 (induced by source paper "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"):
  with members:
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "spot on action localization from pointly supervised proposals"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "segmental spatiotemporal cnns for fine grained action segmentation"
    - "end to end learning of action detection from frame glimpses in videos"

Connected through hyperedge HE-6 (induced by source paper "ava a video dataset of spatio temporally localized atomic visual actions"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "the devil is in the tails fine grained classification in the wild"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "slac a sparsely labeled dataset for action classification and localization"
    - "action tubelet detector for spatio temporal action localization"
    - "the kinetics human action video dataset"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "hacs human action clips and segments dataset for recognition and temporal localization"

Connected through hyperedge HE-7 (induced by source paper "hacs human action clips and segments dataset for recognition and temporal localization"):
  with members:
    - "a short note about kinetics 600"
    - "contextual multi scale region convolutional 3d network for activity detection"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "ctap complementary temporal action proposal generation"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-8 (induced by source paper "from lifestyle vlogs to everyday interactions"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "dilated residual networks"
    - "the kinetics human action video dataset"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "the something something video database for learning and evaluating visual common sense"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "densely connected convolutional networks"
    - "learning action maps of large environments via first person vision"

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.HC(Human-Computer Interaction)