human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "dual triplet metric learning for unsupervised domain adaptation in video based face recognition"):
  with members:
    - "m adda unsupervised domain adaptation with deep metric learning"
    - "domain specific face synthesis for video face recognition from a single sample per person"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "sspp dan deep domain adaptation network for face recognition with single sample per person"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-2 (induced by source paper "dynamonet dynamic action and motion network"):
  with members:
    - "visual dynamics stochastic future generation via layered cross convolutional networks"
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "learning human optical flow"
    - "spatio temporal channel correlation networks for action classification"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "d3d distilled 3d networks for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

Connected through hyperedge HE-3 (induced by source paper "large scale holistic video understanding"):
  with members:
    - "dynamonet dynamic action and motion network"
    - "distinit learning video representations without a single labeled video"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "actionvlad learning spatio temporal aggregation for action classification"

Connected through hyperedge HE-4 (induced by source paper "holistic large scale video understanding"):
  with members:
    - "dynamonet dynamic action and motion network"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"
    - "hacs human action clips and segments dataset for recognition and temporal localization"

Connected through hyperedge HE-5 (induced by source paper "video face clustering with unknown number of clusters"):
  with members:
    - "centroid based deep metric learning for speaker recognition"
    - "learnable pins cross modal embeddings for person identity"
    - "tvqa localized compositional video question answering"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "generating descriptions with grounded and co referenced people"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "revisiting few shot learning for facial expression recognition"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "a closer look at few shot classification"
    - "subspace networks for few shot classification"
    - "on first order meta learning algorithms"
    - "dsfd dual shot face detector"
    - "unsupervised learning of face representations"
    - "low shot learning from imaginary data"

Connected through hyperedge HE-7 (induced by source paper "efficient parameter free clustering using first neighbor relations"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "multi view low rank sparse subspace clustering"
    - "deep continuous clustering"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "clustering millions of faces by identity"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "variational deep embedding an unsupervised and generative approach to clustering"

Question: Please tell me which class the center node 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)