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: "unsupervised learning of face representations"
  Abstract: We present an approach for unsupervised training of CNNs in order to learn discriminative face representations. We mine supervised training data by noting that multiple faces in the same video frame must belong to different persons and the same face tracked across multiple frames must belong to the same person. We obtain millions of face pairs from hundreds of videos without using any manual supervision. Although faces extracted from videos have a lower spatial resolution than those which are available as part of standard supervised face datasets such as LFW and CASIA-WebFace, the former represent a much more realistic setting, e.g. in surveillance scenarios where most of the faces detected are very small. We train our CNNs with the relatively low resolution faces extracted from video frames collected, and achieve a higher verification accuracy on the benchmark LFW dataset cf. hand-crafted features such as LBPs, and even surpasses the performance of state-of-the-art deep networks such as VGG-Face, when they are made to work with low resolution input images.

Connected through hyperedge HE-1 (induced by source paper "revisiting few shot learning for facial expression recognition"):
  with members:
    - "self supervised learning of face representations for video face clustering"
    - "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"
    - "low shot learning from imaginary data"

Connected through hyperedge HE-2 (induced by source paper "self supervised learning of face representations for video face clustering"):
  with members:
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "generating descriptions with grounded and co referenced people"
    - "classification driven dynamic image enhancement"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "moviegraphs towards understanding human centric situations from videos"
    - "learning from video and text via large scale discriminative clustering"
    - "shuffle and learn unsupervised learning using temporal order verification"

Connected through hyperedge HE-3 (induced by source paper "unsupervised learning of eye gaze representation from the web"):
  with members:
    - "prediction and localization of student engagement in the wild"
    - "speech gesture mapping and engagement evaluation in human robot interaction"
    - "unsupervised learning of depth and ego motion from video"
    - "dynamic routing between capsules"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "eye tracking for everyone"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "it s written all over your face full face appearance based gaze estimation"

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)