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: "an attention model for group level emotion recognition"
  Abstract: In this paper we propose a new approach for classifying the global emotion of images containing groups of people. To achieve this task, we consider two different and complementary sources of information: i) a global representation of the entire image (ii) a local representation where only faces are considered. While the global representation of the image is learned with a convolutional neural network (CNN), the local representation is obtained by merging face features through an attention mechanism. The two representations are first learned independently with two separate CNN branches and then fused through concatenation in order to obtain the final group-emotion classifier. For our submission to the EmotiW 2018 group-level emotion recognition challenge, we combine several variations of the proposed model into an ensemble, obtaining a final accuracy of 64.83% on the test set and ranking 4th among all challenge participants.

Connected through hyperedge HE-1 (induced by source paper "multi scale guided attention for medical image segmentation"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "deep q learning driven ct pancreas segmentation with geometry aware u net"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "multiregion segmentation of bladder cancer structures in mri with progressive dilated convolutional networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "mask r cnn with pyramid attention network for scene text detection"

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"
    - "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"
    - "group level emotion recognition using transfer learning from face identification"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "emotiw 2018 audio video student engagement and group level affect prediction"):
  with members:
    - "prediction and localization of student engagement in the wild"
    - "an occam s razor view on learning audiovisual emotion recognition with small training sets"
    - "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)