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: "going deeper in facial expression recognition using deep neural networks"
  Abstract: Automated Facial Expression Recognition (FER) has remained a challenging and interesting problem in computer vision. Despite efforts made in developing various methods for FER, existing approaches lack generalizability when applied to unseen images or those that are captured in wild setting (i.e. the results are not significant). Most of the existing approaches are based on engineered features (e.g. HOG, LBPH, and Gabor) where the classifier's hyper-parameters are tuned to give best recognition accuracies across a single database, or a small collection of similar databases. This paper proposes a deep neural network architecture to address the FER problem across multiple well-known standard face datasets. Specifically, our network consists of two convolutional layers each followed by max pooling and then four Inception layers. The network is a single component architecture that takes registered facial images as the input and classifies them into either of the six basic or the neutral expressions. We conducted comprehensive experiments on seven publicly available facial expression databases, viz. MultiPIE, MMI, CK+, DISFA, FERA, SFEW, and FER2013. The results of our proposed architecture are comparable to or better than the state-of-the-art methods and better than traditional convolutional neural networks in both accuracy and training time.

Connected through hyperedge HE-1 (induced by source paper "joint deep learning of facial expression synthesis and recognition"):
  with members:
    - "using photorealistic face synthesis and domain adaptation to improve facial expression analysis"
    - "deep facial expression recognition a survey"
    - "geometry contrastive gan for facial expression transfer"
    - "sphereface deep hypersphere embedding for face recognition"
    - "geometry guided adversarial facial expression synthesis"
    - "exprgan facial expression editing with controllable expression intensity"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (induced by source paper "learning to augment expressions for few shot fine grained facial expression recognition"):
  with members:
    - "deep learning for human affect recognition insights and new developments"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "improving person re identification by attribute and identity learning"
    - "deep facial expression recognition a survey"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "low shot learning from imaginary data"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"

Connected through hyperedge HE-3 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "eca net efficient channel attention for deep convolutional neural networks"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "region attention networks for pose and occlusion robust facial expression recognition"
    - "frame attention networks for facial expression recognition in videos"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "covariance pooling for facial expression recognition"

Connected through hyperedge HE-4 (induced by source paper "interpretation and simplification of deep forest"):
  with members:
    - "searching for mobilenetv3"
    - "explainable machine learning for scientific insights and discoveries"
    - "multi layered gradient boosting decision trees"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "methods for interpreting and understanding deep neural networks"
    - "deep learning a critical appraisal"
    - "explainable and explicit visual reasoning over scene graphs"

Connected through hyperedge HE-5 (induced by source paper "hyperparameters optimization for deep learning based emotion prediction for human robot interaction"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "social robots for people with developmental disabilities a user study on design features of a graphical user interface"
    - "i know how you feel emotion recognition with facial landmarks"
    - "real time convolutional neural networks for emotion and gender classification"
    - "convolutional neural networks for facial expression recognition"
    - "facial emotion detection using convolutional neural networks and representational autoencoder units"
    - "modeling spatial and temporal cues for multi label facial action unit detection"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "deep multi facial patches aggregation network for facial expression recognition"):
  with members:
    - "learning to augment expressions for few shot fine grained facial expression recognition"
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "deep facial expression recognition a survey"
    - "deep generative contrastive networks for facial expression recognition"
    - "semantic segmentation using adversarial networks"
    - "peak piloted deep network for facial expression recognition"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-7 (induced by source paper "continuous emotion recognition via deep convolutional autoencoder and support vector regressor"):
  with members:
    - "end to end multimodal emotion recognition using deep neural networks"
    - "emotional expression classification using time series kernels"
    - "deep learning using linear support vector machines"

Connected through hyperedge HE-8 (induced by source paper "feratt facial expression recognition with attention net"):
  with members:
    - "fast and robust multiple colorchecker detection using deep convolutional neural networks"
    - "learning dual convolutional neural networks for low level vision"
    - "deep facial expression recognition a survey"
    - "enhanced deep residual networks for single image super resolution"
    - "zero shot super resolution using deep internal learning"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "identity mappings in deep residual networks"
    - "emergence of foveal image sampling from learning to attend in visual scenes"

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.NE(Neural and Evolutionary Computing)