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 "action modifiers learning from adverbs in instructional videos"):
  Abstract: We present a method to learn a representation for adverbs from instructional videos using weak supervision from the accompanying narrations. Key to our method is the fact that the visual representation of the adverb is highly dependant on the action to which it applies, although the same adverb will modify multiple actions in a similar way. For instance, while 'spread quickly' and 'mix quickly' will look dissimilar, we can learn a common representation that allows us to recognize both, among other actions. We formulate this as an embedding problem, and use scaled dot-product attention to learn from weakly-supervised video narrations. We jointly learn adverbs as invertible transformations operating on the embedding space, so as to add or remove the effect of the adverb. As there is no prior work on weakly supervised learning from adverbs, we gather paired action-adverb annotations from a subset of the HowTo100M dataset for 6 adverbs: quickly/slowly, finely/coarsely, and partially/completely. Our method outperforms all baselines for video-to-adverb retrieval with a performance of 0.719 mAP. We also demonstrate our model's ability to attend to the relevant video parts in order to determine the adverb for a given action.

Includes members:
  Member "manipulation skill assessment from videos with spatial attention network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding contexts inside robot and human manipulation tasks through a vision language model and ontology system in a video stream")
  Member "weakly supervised video moment retrieval from text queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences")
  Member "howto100m learning a text video embedding by watching hundred million narrated video clips":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "wman weakly supervised moment alignment network for text based video segment retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised multi level attentional reconstruction network for grounding textual queries in videos")
  Member "human action adverb recognition adha dataset and a three stream hybrid model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep rnn framework for visual sequential applications")
  Member "adversarial soft detection based aggregation network for image retrieval":
    also connected through other hyperedges:
      (none)
  Member "motion appearance co memory networks for video question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "attributes as operators factorizing unseen attribute object compositions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to learn words from visual scenes")

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)