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: "photorealistic facial synthesis in the dimensional affect space"
  Abstract: This paper presents a novel approach for synthesizing facial affect, which is based on our annotating 600,000 frames of the 4DFAB database in terms of valence and arousal. The input of this approach is a pair of these emotional state descriptors and a neutral 2D image of a person to whom the corresponding affect will be synthesized. Given this target pair, a set of 3D facial meshes is selected, which is used to build a blendshape model and generate the new facial affect. To synthesize the affect on the 2D neutral image, 3DMM fitting is performed and the reconstructed face is deformed to generate the target facial expressions. Last, the new face is rendered into the original image. Both qualitative and quantitative experimental studies illustrate the generation of realistic images, when the neutral image is sampled from a variety of well known databases, such as the Aff-Wild, AFEW, Multi-PIE, AFEW-VA, BU-3DFE, Bosphorus.

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"
    - "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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "emotion generation and recognition a stargan approach"):
  with members:
    - "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"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "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"
    - "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"
    - "an occam s razor view on learning audiovisual emotion recognition with small training sets"
    - "deep facial expression recognition a survey"

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"
    - "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"
    - "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"

Connected through hyperedge HE-5 (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"
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "a multi task learning generation framework valence arousal action units primary expressions"):
  with members:
    - "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"
    - "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"
    - "began boundary equilibrium generative adversarial networks"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "aff wild2 extending the aff wild database for affect recognition"):
  with members:
    - "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"
    - "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"
    - "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.GR(Graphics)