human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "step by step erasion one by one collection a weakly supervised temporal action detector"):
  Abstract: Weakly supervised temporal action detection is a Herculean task in understanding untrimmed videos, since no supervisory signal except the video-level category label is available on training data. Under the supervision of category labels, weakly supervised detectors are usually built upon classifiers. However, there is an inherent contradiction between classifier and detector; i.e., a classifier in pursuit of high classification performance prefers top-level discriminative video clips that are extremely fragmentary, whereas a detector is obliged to discover the whole action instance without missing any relevant snippet. To reconcile this contradiction, we train a detector by driving a series of classifiers to find new actionness clips progressively, via step-by-step erasion from a complete video. During the test phase, all we need to do is to collect detection results from the one-by-one trained classifiers at various erasing steps. To assist in the collection process, a fully connected conditional random field is established to refine the temporal localization outputs. We evaluate our approach on two prevailing datasets, THUMOS'14 and ActivityNet. The experiments show that our detector advances state-of-the-art weakly supervised temporal action detection results, and even compares with quite a few strongly supervised methods.

Includes members:
  Member "monocular depth estimation using multi scale continuous crfs as sequential deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields")
  Member "learning spatio temporal representation with pseudo 3d residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos")
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "single shot temporal action detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sf net single frame supervision for temporal action localization")
  Member "temporal action detection with structured segment networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "temporal segment networks for action recognition in videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semantic image networks for human action recognition")
  Member "object region mining with adversarial erasing a simple classification to semantic segmentation approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "explainable deep classification models for domain generalization")
  Member "untrimmednets for weakly supervised action recognition and detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")

Question: Please tell me which class the source paper 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)