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 "noise estimation using density estimation for self supervised multimodal learning"):
  Abstract: One of the key factors of enabling machine learning models to comprehend and solve real-world tasks is to leverage multimodal data. Unfortunately, annotation of multimodal data is challenging and expensive. Recently, self-supervised multimodal methods that combine vision and language were proposed to learn multimodal representations without annotation. However, these methods choose to ignore the presence of high levels of noise and thus yield sub-optimal results. In this work, we show that the problem of noise estimation for multimodal data can be reduced to a multimodal density estimation task. Using multimodal density estimation, we propose a noise estimation building block for multimodal representation learning that is based strictly on the inherent correlation between different modalities. We demonstrate how our noise estimation can be broadly integrated and achieves comparable results to state-of-the-art performance on five different benchmark datasets for two challenging multimodal tasks: Video Question Answering and Text-To-Video Retrieval.

Includes members:
  Member "end to end learning of visual representations from uncurated instructional videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi modal self supervision from generalized data transformations")
  Member "use what you have video retrieval using representations from collaborative experts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fine grained video text retrieval with hierarchical graph reasoning")
  Member "polysemous visual semantic embedding for cross modal retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "target oriented deformation of visual semantic embedding space")
  Member "self supervised visual feature learning with deep neural networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "proposal learning for semi supervised object detection")
  Member "videobert a joint model for video and language representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "grounding object detections with transcriptions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "end to end learning of visual representations from uncurated instructional videos")
  Member "heterogeneous memory enhanced multimodal attention model for video question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical conditional relation networks for video question answering")
  Member "learning to detect and retrieve objects from unlabeled videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "end to end learning of visual representations from uncurated instructional videos")

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)