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: "generating faces for affect analysis"
  Abstract: This paper presents a novel approach for synthesizing facial affect; either categorical, in terms of the six basic expressions (i.e., anger, disgust, fear, happiness, sadness and surprise), or dimensional, in terms of valence (i.e., how positive or negative is an emotion) and arousal (i.e., power of the emotion activation). In the Valence-Arousal case, a system is created, based on VA annotation of 600,000 frames from the 4DFAB database; in the categorical case, the system is based on the selection of apex frames of posed expression sequences from the 4DFAB. The proposed system accepts at its input: i) either the basic facial expression, or the pair of valence-arousal emotional state descriptors, which need to be synthesized and ii) a neutral 2D image of a person on which the corresponding affect will be synthesized. The proposed approach consists of the following steps: First, based on the provided desired emotional state, a set of 3D facial meshes is produced from the 4DFAB database and is used to build a blendshape model that generates the new facial affect. To synthesize this affect on the 2D neutral image, 3D Morphable Models fitting is performed and the reconstructed face is then deformed to generate the target facial expressions. Finally, the new face is rendered into the original image. Qualitative experimental studies illustrate the generation of realistic images, when the neutral image is sampled from a variety of well known lab-controlled or in-the-wild databases, including Aff-Wild, RECOLA, AffectNet, AFEW, Multi-PIE, AFEW-VA, BU-3DFE, Bosphorus, RAF-DB. Also, quantitative experiments are conducted, in which deep neural networks, trained using the generated images from each of the above databases in a data-augmentation framework, provide affect recognition; better performances are achieved through the presented approach when compared with the current state-of-the-art.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "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-3 (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"
    - "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-4 (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"
    - "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-5 (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"
    - "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-6 (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"
    - "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.CV(Computer Vision and Pattern Recognition)