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: "training deep networks for facial expression recognition with crowd sourced label distribution"
  Abstract: Crowd sourcing has become a widely adopted scheme to collect ground truth labels. However, it is a well-known problem that these labels can be very noisy. In this paper, we demonstrate how to learn a deep convolutional neural network (DCNN) from noisy labels, using facial expression recognition as an example. More specifically, we have 10 taggers to label each input image, and compare four different approaches to utilizing the multiple labels: majority voting, multi-label learning, probabilistic label drawing, and cross-entropy loss. We show that the traditional majority voting scheme does not perform as well as the last two approaches that fully leverage the label distribution. An enhanced FER+ data set with multiple labels for each face image will also be shared with the research community.

Connected through hyperedge HE-1 (induced by source paper "expression analysis based on face regions in read world conditions"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "attention is all you need"
    - "affect lm a neural language model for customizable affective text generation"
    - "dual path networks"
    - "affective neural response generation"
    - "from facial expression recognition to interpersonal relation prediction"
    - "densely connected convolutional networks"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-2 (induced by source paper "suppressing uncertainties for large scale facial expression recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "noise tolerant paradigm for training face recognition cnns"
    - "region attention networks for pose and occlusion robust facial expression recognition"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "arcface additive angular margin loss for deep face recognition"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-3 (induced by source paper "beyond without forgetting multi task learning for classification with disjoint datasets"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "semisupervised feature analysis by mining correlations among multiple tasks"
    - "learning from noisy labels with distillation"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-4 (induced by source paper "efficient facial feature learning with wide ensemble based 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"
    - "cnn based facial affect analysis on mobile devices"
    - "meal multi model ensemble via adversarial learning"
    - "efficient diverse ensemble for discriminative co tracking"
    - "xception deep learning with depthwise separable convolutions"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (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-6 (induced by source paper "benanza automatic mu benchmark generation to compute lower bound latency and inform optimizations of deep learning models on gpus"):
  with members:
    - "a survey on compiler autotuning using machine learning"
    - "miopen an open source library for deep learning primitives"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "xsp across stack profiling and analysis of machine learning models on gpus"
    - "neurovectorizer end to end vectorization with deep reinforcement learning"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "arcface additive angular margin loss for deep face recognition"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-7 (induced by source paper "modelhub ai dissemination platform for deep learning models"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"
    - "dlhub model and data serving for science"
    - "unsupervised odometry and depth learning for endoscopic capsule robots"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "nips not even wrong a systematic review of empirically complete demonstrations of algorithmic effectiveness in the machine learning and artificial intelligence literature"
    - "tensorflow serving flexible high performance ml serving"

Connected through hyperedge HE-8 (induced by source paper "feratt facial expression recognition with attention net"):
  with members:
    - "fast and robust multiple colorchecker detection using deep convolutional neural networks"
    - "learning dual convolutional neural networks for low level vision"
    - "deep facial expression recognition a survey"
    - "enhanced deep residual networks for single image super resolution"
    - "zero shot super resolution using deep internal learning"
    - "identity mappings in deep residual networks"
    - "going deeper in facial expression recognition using deep neural networks"
    - "emergence of foveal image sampling from learning to attend in visual scenes"

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)