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 "self supervised learning of class embeddings from video"):
  Abstract: This work explores how to use self-supervised learning on videos to learn a class-specific image embedding that encodes pose and shape information. At train time, two frames of the same video of an object class (e.g. human upper body) are extracted and each encoded to an embedding. Conditioned on these embeddings, the decoder network is tasked to transform one frame into another. To successfully perform long range transformations (e.g. a wrist lowered in one image should be mapped to the same wrist raised in another), we introduce a hierarchical probabilistic network decoder model. Once trained, the embedding can be used for a variety of downstream tasks and domains. We demonstrate our approach quantitatively on three distinct deformable object classes -- human full bodies, upper bodies, faces -- and show experimentally that the learned embeddings do indeed generalise. They achieve state-of-the-art performance in comparison to other self-supervised methods trained on the same datasets, and approach the performance of fully supervised methods.

Includes members:
  Member "learning landmarks from unaligned data using image translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised learning of landmarks by descriptor vector exchange")
  Member "growing a brain fine tuning by increasing model capacity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scaling laws for neural language models")
  Member "unsupervised learning of object landmarks through conditional image generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "chained representation cycling learning to estimate 3d human pose and shape by cycling between representations")
  Member "self supervised learning of a facial attribute embedding from video":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "self supervised spatiotemporal feature learning via video rotation prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "unsupervised discovery of object landmarks as structural representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency")
  Member "a fully progressive approach to single image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fine grained visual classification via progressive multi granularity training of jigsaw patches")
  Member "voxceleb2 deep speaker recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "everybody s talkin let me talk as you want")

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)