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: "group level emotion recognition using transfer learning from face identification"
  Abstract: In this paper, we describe our algorithmic approach, which was used for submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017) group-level emotion recognition sub-challenge. We extracted feature vectors of detected faces using the Convolutional Neural Network trained for face identification task, rather than traditional pre-training on emotion recognition problems. In the final pipeline an ensemble of Random Forest classifiers was learned to predict emotion score using available training set. In case when the faces have not been detected, one member of our ensemble extracts features from the whole image. During our experimental study, the proposed approach showed the lowest error rate when compared to other explored techniques. In particular, we achieved 75.4% accuracy on the validation data, which is 20% higher than the handcrafted feature-based baseline. The source code using Keras framework is publicly available.

Connected through hyperedge HE-1 (induced by source paper "efficient facial representations for age gender and identity recognition in organizing photo albums using multi output convnet"):
  with members:
    - "unconstrained face identification using maximum likelihood of distances between deep off the shelf features"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "face clustering representation and pairwise constraints"
    - "merge or not learning to group faces via imitation learning"
    - "organizing multimedia data in video surveillance systems based on face verification with convolutional neural networks"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "non volume preserving based feature fusion approach to group level expression recognition on crowd videos"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "an attention model for group level emotion recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "neonatal pain expression recognition using transfer learning"):
  with members:
    - "deepfacelift interpretable personalized models for automatic estimation of self reported pain"
    - "machine based multimodal pain assessment tool for infants a review"
    - "return of the devil in the details delving deep into convolutional nets"
    - "matconvnet convolutional neural networks for matlab"

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)