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 occam s razor view on learning audiovisual emotion recognition with small training sets"
  Abstract: This paper presents a light-weight and accurate deep neural model for audiovisual emotion recognition. To design this model, the authors followed a philosophy of simplicity, drastically limiting the number of parameters to learn from the target datasets, always choosing the simplest earning methods: i) transfer learning and low-dimensional space embedding allows to reduce the dimensionality of the representations. ii) The isual temporal information is handled by a simple score-per-frame selection process, averaged across time. iii) A simple frame selection echanism is also proposed to weight the images of a sequence. iv) The fusion of the different modalities is performed at prediction level (late usion). We also highlight the inherent challenges of the AFEW dataset and the difficulty of model selection with as few as 383 validation equences. The proposed real-time emotion classifier achieved a state-of-the-art accuracy of 60.64 % on the test set of AFEW, and ranked 4th at he Emotion in the Wild 2018 challenge.

Connected through hyperedge HE-1 (induced by source paper "towards a general model of knowledge for facial analysis by multi source transfer learning"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "delta deep learning transfer using feature map with attention for convolutional networks"
    - "mfas multimodal fusion architecture search"
    - "task2vec task embedding for meta learning"
    - "best sources forward domain generalization through source specific nets"
    - "measure manifold learning and optimization a theory of neural networks"
    - "deep clustering for unsupervised learning of visual features"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-2 (induced by source paper "ai in pursuit of happiness finding only sadness multi modal facial emotion recognition challenge"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "generating faces for affect analysis"
    - "deep facial expression recognition a survey"
    - "photorealistic facial synthesis in the dimensional affect space"

Connected through hyperedge HE-3 (induced by source paper "e2 capsule neural networks for facial expression recognition using au aware attention"):
  with members:
    - "feratt facial expression recognition with attention net"
    - "generative image inpainting with contextual attention"
    - "dynamic routing between capsules"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "to trust or not to trust a study of human bias in automated video interview assessments"):
  with members:
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-5 (induced by source paper "the many moods of emotion"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep video portraits"
    - "covariance pooling for facial expression recognition"
    - "ganimation anatomically aware facial animation from a single image"
    - "cake compact and accurate k dimensional representation of emotion"
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "geometry guided adversarial facial expression synthesis"

Connected through hyperedge HE-6 (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 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.AI(Artificial Intelligence)