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 face representations for video face clustering"):
  Abstract: Analyzing the story behind TV series and movies often requires understanding who the characters are and what they are doing. With improving deep face models, this may seem like a solved problem. However, as face detectors get better, clustering/identification needs to be revisited to address increasing diversity in facial appearance. In this paper, we address video face clustering using unsupervised methods. Our emphasis is on distilling the essential information, identity, from the representations obtained using deep pre-trained face networks. We propose a self-supervised Siamese network that can be trained without the need for video/track based supervision, and thus can also be applied to image collections. We evaluate our proposed method on three video face clustering datasets. The experiments show that our methods outperform current state-of-the-art methods on all datasets. Video face clustering is lacking a common benchmark as current works are often evaluated with different metrics and/or different sets of face tracks.

Includes members:
  Member "unsupervised learning of face representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting few shot learning for facial expression recognition")
  Member "from benedict cumberbatch to sherlock holmes character identification in tv series without a script":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowit vqa answering knowledge based questions about videos")
  Member "vggface2 a dataset for recognising faces across pose and age":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "generating descriptions with grounded and co referenced people":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "visual grounding in video for unsupervised word translation")
  Member "classification driven dynamic image enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamonet dynamic action and motion network")
  Member "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "diversity achieving slow dropblock network for person re identification")
  Member "moviegraphs towards understanding human centric situations from videos":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning diverse fashion collocation by neural graph filtering")
  Member "learning from video and text via large scale discriminative clustering":
    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)