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: "prediction and localization of student engagement in the wild"
  Abstract: Student engagement localization can play a key role in designing successful e-learning systems. Facial expressions of an individual in response to stimuli videos provide important cues in estimating variations in engagement level. In this paper we study the association of a \textbf{subject}'s facial expressions with his/her engagement level, as annotated by labelers. We then localize engaging/non-engaging parts in the stimuli videos using a deep multiple instance learning based framework, which can give useful insight into designing Massive Open Online Courses (MOOCs) video material. Recognizing the lack of any publicly available dataset in the domain of user engagement, a new `in the wild' database is created to study subject engagement. The dataset contains 725,000 frames (about 9 hours of recording), 102 videos captured from 75 subjects. The problem of engagement prediction is modeled as a weak label learning problem. The dataset is manually annotated by labelers for four levels of engagement and the predicted labels of videos are correlated with the manual labels. This dataset creation is an effort to facilitate research in various e-learning environments such as intelligent tutoring systems , MOOCs and others.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning of eye gaze representation from the web"):
  with members:
    - "unsupervised learning of face representations"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "bootstrap model ensemble and rank loss for engagement intensity regression"):
  with members:
    - "region attention networks for pose and occlusion robust facial expression recognition"
    - "frame attention networks for facial expression recognition in videos"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "emotiw 2018 audio video student engagement and group level affect prediction"):
  with members:
    - "an occam s razor view on learning audiovisual emotion recognition with small training sets"
    - "an attention model for group level emotion recognition"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"

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)