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: "incremental boosting convolutional neural network for facial action unit recognition"
  Abstract: Recognizing facial action units (AUs) from spontaneous facial expressions is still a challenging problem. Most recently, CNNs have shown promise on facial AU recognition. However, the learned CNNs are often overfitted and do not generalize well to unseen subjects due to limited AU-coded training images. We proposed a novel Incremental Boosting CNN (IB-CNN) to integrate boosting into the CNN via an incremental boosting layer that selects discriminative neurons from the lower layer and is incrementally updated on successive mini-batches. In addition, a novel loss function that accounts for errors from both the incremental boosted classifier and individual weak classifiers was proposed to fine-tune the IB-CNN. Experimental results on four benchmark AU databases have demonstrated that the IB-CNN yields significant improvement over the traditional CNN and the boosting CNN without incremental learning, as well as outperforming the state-of-the-art CNN-based methods in AU recognition. The improvement is more impressive for the AUs that have the lowest frequencies in the databases.

Connected through hyperedge HE-1 (induced by source paper "robust regression via deep negative correlation learning"):
  with members:
    - "residual non local attention networks for image restoration"
    - "bag of tricks for image classification with convolutional neural networks"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "explaining first impressions modeling recognizing and explaining apparent personality from videos"
    - "residual dense network for image super resolution"
    - "image super resolution via dual state recurrent networks"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

Connected through hyperedge HE-2 (induced by source paper "collaborative training of balanced random forests for open set domain adaptation"):
  with members:
    - "weakly supervised open set domain adaptation by dual domain collaboration"
    - "domain symmetric networks for adversarial domain adaptation"
    - "algorithms and theory for multiple source adaptation"
    - "open set domain adaptation by backpropagation"
    - "boosting domain adaptation by discovering latent domains"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "partial transfer learning with selective adversarial networks"

Connected through hyperedge HE-3 (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-4 (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"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "mask r cnn"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"

Connected through hyperedge HE-5 (induced by source paper "attentionboost learning what to attend by boosting fully convolutional networks"):
  with members:
    - "image segmentation by iterative inference from conditional score estimation"
    - "focal loss for dense object detection"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "gland instance segmentation using deep multichannel neural networks"
    - "a survey on deep learning in medical image analysis"
    - "gland instance segmentation by deep multichannel side supervision"
    - "gland segmentation in colon histology images the glas challenge contest"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "binary ensemble neural network more bits per network or more networks per bit"):
  with members:
    - "model compression via distillation and quantization"
    - "towards fast and energy efficient binarized neural network inference on fpga"
    - "network sketching exploiting binary structure in deep cnns"
    - "gated xnor networks deep neural networks with ternary weights and activations under a unified discretization framework"
    - "the high dimensional geometry of binary neural networks"
    - "training quantized nets a deeper understanding"
    - "towards accurate binary convolutional neural network"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-7 (induced by source paper "deep metric learning with bier boosting independent embeddings robustly"):
  with members:
    - "adversarial discriminative domain adaptation"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "smart mining for deep metric learning"
    - "siamese instance search for tracking"
    - "deep metric learning via facility location"
    - "local similarity aware deep feature embedding"

Connected through hyperedge HE-8 (induced by source paper "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis"):
  with members:
    - "recurrent convolutional neural network regression for continuous pain intensity estimation in video"
    - "multi instance dynamic ordinal random fields for weakly supervised pain intensity estimation"
    - "variable state latent conditional random fields for facial expression recognition and action unit detection"
    - "multiple instance learning by discriminative training of markov networks"
    - "fast exact inference for recursive cardinality models"

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)