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: "facial expression recognition using enhanced deep 3d convolutional neural networks"
  Abstract: Deep Neural Networks (DNNs) have shown to outperform traditional methods in various visual recognition tasks including Facial Expression Recognition (FER). In spite of efforts made to improve the accuracy of FER systems using DNN, existing methods still are not generalizable enough in practical applications. This paper proposes a 3D Convolutional Neural Network method for FER in videos. This new network architecture consists of 3D Inception-ResNet layers followed by an LSTM unit that together extracts the spatial relations within facial images as well as the temporal relations between different frames in the video. Facial landmark points are also used as inputs to our network which emphasize on the importance of facial components rather than the facial regions that may not contribute significantly to generating facial expressions. Our proposed method is evaluated using four publicly available databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "emptransfo a multi head transformer architecture for creating empathetic dialog systems"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "the curious case of neural text degeneration"
    - "delivering cognitive behavioral therapy using a conversational socialrobot"
    - "huggingface s transformers state of the art natural language processing"
    - "affect driven dialog generation"
    - "bleu is not suitable for the evaluation of text simplification"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-2 (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-3 (induced by source paper "timeconvnets a deep time windowed convolution neural network design for real time video facial expression recognition"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "real time convolutional neural networks for emotion and gender classification"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "a real time facial expression classification system using local binary patterns"

Connected through hyperedge HE-4 (induced by source paper "inferring dynamic representations of facial actions from a still image"):
  with members:
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "a functional regression approach to facial landmark tracking"
    - "action anticipation by predicting future dynamic images"
    - "ganimation anatomically aware facial animation from a single image"
    - "joint action unit localisation and intensity estimation through heatmap regression"
    - "deep structured learning for facial action unit intensity estimation"
    - "deepcoder semi parametric variational autoencoders for automatic facial action coding"
    - "rank pooling for action recognition"

Connected through hyperedge HE-5 (induced by source paper "recognizing facial expressions of occluded faces using convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "improving bag of visual words towards effective facial expressive image classification"
    - "facial expression recognition using a hybrid cnn sift aggregator"
    - "eyemotion classifying facial expressions in vr using eye tracking cameras"
    - "facial expression recognition from world wild web"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"

Connected through hyperedge HE-6 (induced by source paper "bounded residual gradient networks breg net for facial affect computing"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "self supervised learning of a facial attribute embedding from video"
    - "facial affect estimation in the wild using deep residual and convolutional networks"
    - "facial expression recognition from world wild web"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "going deeper in facial expression recognition using deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "2d and 3d segmentation of uncertain local collagen fiber orientations in shg microscopy"):
  with members:
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "focal loss for dense object detection"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "a deeper look at facial expression dataset bias"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "spatio temporal facial expression recognition using convolutional neural networks and conditional random fields"
    - "going deeper in facial expression recognition using deep neural networks"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"

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)