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 "learning spatiotemporal features via video and text pair discrimination"):
  Abstract: Current video representations heavily rely on learning from manually annotated video datasets. However, it is expensive and time-consuming to acquire a large-scale well-labeled video dataset. We observe that videos are naturally accompanied with abundant text information such as YouTube titles and movie scripts. In this paper, we leverage this visual-textual connection to learn effective spatiotemporal features in an efficient weakly-supervised manner. We present a general cross-modal pair discrimination (CPD) framework to capture this correlation between a clip and its associated text, and adopt noise-contrastive estimation technique to tackle the computational issues imposed by the huge numbers of pair instance classes. Specifically, we investigate the CPD framework from two sources of video-text pairs, and design a practical curriculum learning strategy to train the CPD. Without further fine tuning, the learned models obtain competitive results for action classification on the Kinetics dataset under the common linear classification protocol. Moreover, our visual model provides a very effective initialization to fine-tune on the downstream task datasets. Experimental results demonstrate that our weakly-supervised pre-training yields a remarkable performance gain for action recognition on the datasets of UCF101 and HMDB51, compared with the state-of-the-art self-supervised training methods. In addition, our CPD model yields a new state of the art for zero-shot action recognition on UCF101 by directly utilizing the learnt visual-textual embedding.

Includes members:
  Member "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi modal self supervision from generalized data transformations")
  Member "distilbert a distilled version of bert smaller faster cheaper and lighter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "contrastive multiview coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning representations by predicting bags of visual words")
  Member "universal dependency parsing from scratch":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "grounded and controllable image completion by incorporating lexical semantics")
  Member "dynamonet dynamic action and motion network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "learning correspondence from the cycle consistency of time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "videobert a joint model for video and language representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "howto100m learning a text video embedding by watching hundred million narrated video clips":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")

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)