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: "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"
  Abstract: This paper presents a novel CNN-RNN based approach, which exploits multiple CNN features for dimensional emotion recognition in-the-wild, utilizing the One-Minute Gradual-Emotion (OMG-Emotion) dataset. Our approach includes first pre-training with the relevant and large in size, Aff-Wild and Aff-Wild2 emotion databases. Low-, mid- and high-level features are extracted from the trained CNN component and are exploited by RNN subnets in a multi-task framework. Their outputs constitute an intermediate level prediction; final estimates are obtained as the mean or median values of these predictions. Fusion of the networks is also examined for boosting the obtained performance, at Decision-, or at Model-level; in the latter case a RNN was used for the fusion. Our approach, although using only the visual modality, outperformed state-of-the-art methods that utilized audio and visual modalities. Some of our developments have been submitted to the OMG-Emotion Challenge, ranking second among the technologies which used only visual information for valence estimation; ranking third overall. Through extensive experimentation, we further show that arousal estimation is greatly improved when low-level features are combined with high-level ones.

Connected through hyperedge HE-1 (induced by source paper "analysing affective behavior in the first abaw 2020 competition"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "avec 2019 workshop and challenge state of mind detecting depression with ai and cross cultural affect recognition"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "facial expression recognition using disentangled adversarial learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "efficient augmentation via data subsampling"
    - "a multi task learning generation framework valence arousal action units primary expressions"

Connected through hyperedge HE-2 (induced by source paper "emotion generation and recognition a stargan approach"):
  with members:
    - "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"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "photorealistic facial synthesis in the dimensional affect space"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-3 (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"
    - "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"
    - "an occam s razor view on learning audiovisual emotion recognition with small training sets"
    - "deep facial expression recognition a survey"
    - "photorealistic facial synthesis in the dimensional affect space"

Connected through hyperedge HE-4 (induced by source paper "aff wild database and affwildnet"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "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"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "generating faces for affect analysis"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"

Connected through hyperedge HE-5 (induced by source paper "interpretable deep neural networks for facial expression and dimensional emotion recognition in the wild"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "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"
    - "transfer learning for action unit recognition"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "joint unsupervised learning of deep representations and image clusters"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "a multi component cnn rnn approach for dimensional emotion recognition in the wild"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "face behavior a la carte expressions affect and action units in a single network"
    - "the omg emotion behavior dataset"
    - "generating faces for affect analysis"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "deep neural network augmentation generating faces for affect analysis"
    - "aff wild2 extending the aff wild database for affect recognition"

Connected through hyperedge HE-7 (induced by source paper "a multi task learning generation framework valence arousal action units primary expressions"):
  with members:
    - "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"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "photorealistic facial synthesis in the dimensional affect space"
    - "began boundary equilibrium generative adversarial networks"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "aff wild2 extending the aff wild database for affect recognition"):
  with members:
    - "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"
    - "photorealistic facial synthesis in the dimensional affect space"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "deep residual learning for image recognition"
    - "densenet implementing efficient convnet descriptor pyramids"

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.LG(Machine Learning)