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: "peak piloted deep network for facial expression recognition"
  Abstract: Objective functions for training of deep networks for face-related recognition tasks, such as facial expression recognition (FER), usually consider each sample independently. In this work, we present a novel peak-piloted deep network (PPDN) that uses a sample with peak expression (easy sample) to supervise the intermediate feature responses for a sample of non-peak expression (hard sample) of the same type and from the same subject. The expression evolving process from non-peak expression to peak expression can thus be implicitly embedded in the network to achieve the invariance to expression intensities. A special purpose back-propagation procedure, peak gradient suppression (PGS), is proposed for network training. It drives the intermediate-layer feature responses of non-peak expression samples towards those of the corresponding peak expression samples, while avoiding the inverse. This avoids degrading the recognition capability for samples of peak expression due to interference from their non-peak expression counterparts. Extensive comparisons on two popular FER datasets, Oulu-CASIA and CK+, demonstrate the superiority of the PPDN over state-ofthe-art FER methods, as well as the advantages of both the network structure and the optimization strategy. Moreover, it is shown that PPDN is a general architecture, extensible to other tasks by proper definition of peak and non-peak samples. This is validated by experiments that show state-of-the-art performance on pose-invariant face recognition, using the Multi-PIE dataset.

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 "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-3 (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"
    - "going deeper in facial expression recognition using deep neural networks"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "region attention networks for pose and occlusion robust facial expression recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "frame attention networks for facial expression recognition in videos"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "deep facial expression recognition a survey"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "island loss for learning discriminative features in facial expression recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"):
  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"
    - "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"
    - "the omg emotion behavior dataset"
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "a deep network for arousal valence emotion prediction with acoustic visual cues"
    - "audeering s approach to the one minute gradual emotion challenge"

Connected through hyperedge HE-6 (induced by source paper "deep emotion facial expression recognition using attentional convolutional network"):
  with members:
    - "spatial temporal recurrent neural network for emotion recognition"
    - "ad net audio visual convolutional neural network for advertisement detection in videos"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "an experimental study of deep convolutional features for iris recognition"
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "mask r cnn"

Connected through hyperedge HE-7 (induced by source paper "dynamic multi task learning for face recognition with facial expression"):
  with members:
    - "multi task convolutional neural network for pose invariant face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "an overview of multi task learning in deep neural networks"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "going deeper in facial expression recognition using deep neural networks"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "facelivenet a holistic networks for face authentication based on dynamic multi task convolutional neural networks"):
  with members:
    - "multi task convolutional neural network for pose invariant face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "an overview of multi task learning in deep neural networks"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "a multi task deep network for person re identification"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"

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)