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: "diagnosing error in temporal action detectors"
  Abstract: Despite the recent progress in video understanding and the continuous rate of improvement in temporal action localization throughout the years, it is still unclear how far (or close?) we are to solving the problem. To this end, we introduce a new diagnostic tool to analyze the performance of temporal action detectors in videos and compare different methods beyond a single scalar metric. We exemplify the use of our tool by analyzing the performance of the top rewarded entries in the latest ActivityNet action localization challenge. Our analysis shows that the most impactful areas to work on are: strategies to better handle temporal context around the instances, improving the robustness w.r.t. the instance absolute and relative size, and strategies to reduce the localization errors. Moreover, our experimental analysis finds the lack of agreement among annotator is not a major roadblock to attain progress in the field. Our diagnostic tool is publicly available to keep fueling the minds of other researchers with additional insights about their algorithms.

Connected through hyperedge HE-1 (induced by source paper "refineloc iterative refinement for weakly supervised action localization"):
  with members:
    - "3c net category count and center loss for weakly supervised action localization"
    - "slim densepose thrifty learning from sparse annotations and motion cues"
    - "unsupervised pre training of image features on non curated data"
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "weakly supervised action localization with background modeling"
    - "bmn boundary matching network for temporal action proposal generation"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "deep learning is robust to massive label noise"

Connected through hyperedge HE-2 (induced by source paper "g tad sub graph localization for temporal action detection"):
  with members:
    - "gaussian temporal awareness networks for action localization"
    - "learning video representations from correspondence proposals"
    - "graph convolutional networks for temporal action localization"
    - "mesh r cnn"
    - "bmn boundary matching network for temporal action proposal generation"
    - "deepgcns can gcns go as deep as cnns"
    - "point clouds learning with attention based graph convolution networks"
    - "multi granularity generator for temporal action proposal"

Connected through hyperedge HE-3 (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"
    - "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"
    - "rethinking the faster r cnn architecture for temporal action localization"

Connected through hyperedge HE-4 (induced by source paper "a context aware loss function for action spotting in soccer videos"):
  with members:
    - "golfdb a video database for golf swing sequencing"
    - "gaussian temporal awareness networks for action localization"
    - "pan tilt zoom slam for sports videos"
    - "bmn boundary matching network for temporal action proposal generation"
    - "fine grained activity recognition in baseball videos"
    - "temporal hockey action recognition via pose and optical flows"
    - "taskonomy disentangling task transfer learning"
    - "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"

Connected through hyperedge HE-5 (induced by source paper "tracklinic diagnosis of challenge factors in visual tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "roi pooled correlation filters for visual tracking"
    - "skimming perusal tracking a framework for real time and robust long term tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "atom accurate tracking by overlap maximization"
    - "long term tracking in the wild a benchmark"

Connected through hyperedge HE-6 (induced by source paper "temporal localization of moments in video collections with natural language"):
  with members:
    - "do better imagenet models transfer better"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "deep contextualized word representations"
    - "multilevel language and vision integration for text to clip retrieval"
    - "ctap complementary temporal action proposal generation"
    - "video object segmentation with language referring expressions"
    - "cross modal and hierarchical modeling of video and text"

Connected through hyperedge HE-7 (induced by source paper "blp boundary likelihood pinpointing networks for accurate temporal action localization"):
  with members:
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "soft nms improving object detection with one line of code"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "compressed video action recognition"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "cascaded boundary regression for temporal action detection"

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)