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: "emotiw 2018 audio video student engagement and group level affect prediction"
  Abstract: This paper details the sixth Emotion Recognition in the Wild (EmotiW) challenge. EmotiW 2018 is a grand challenge in the ACM International Conference on Multimodal Interaction 2018, Colorado, USA. The challenge aims at providing a common platform to researchers working in the affective computing community to benchmark their algorithms on `in the wild' data. This year EmotiW contains three sub-challenges: a) Audio-video based emotion recognition; b) Student engagement prediction; and c) Group-level emotion recognition. The databases, protocols and baselines are discussed in detail.

Connected through hyperedge HE-1 (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"
    - "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"
    - "face attention network an effective face detector for the occluded faces"

Connected through hyperedge HE-2 (induced by source paper "a personalized affective memory neural model for improving emotion recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
    - "the omg emotion behavior dataset"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "unsupervised deep representations for learning audience facial behaviors"
    - "a deep network for arousal valence emotion prediction with acoustic visual cues"
    - "multimodal emotion recognition for one minute gradual emotion challenge"
    - "geometry guided adversarial facial expression synthesis"

Connected through hyperedge HE-3 (induced by source paper "face behavior a la carte expressions affect and action units in a single network"):
  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"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "two level attention with two stage multi task learning for facial emotion recognition"
    - "emotionet challenge recognition of facial expressions of emotion in the wild"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"

Connected through hyperedge HE-4 (induced by source paper "frame attention networks for facial expression recognition in videos"):
  with members:
    - "island loss for learning discriminative features in facial expression recognition"
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "attention is all you need"
    - "learning to compare relation network for few shot learning"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "neural aggregation network for video face recognition"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "lomo latent ordinal model for facial analysis in videos"

Connected through hyperedge HE-5 (induced by source paper "feature level and model level audiovisual fusion for emotion recognition in the wild"):
  with members:
    - "identity free facial expression recognition using conditional generative adversarial network"
    - "deep texture manifold for ground terrain recognition"
    - "probabilistic attribute tree in convolutional neural networks for facial expression recognition"
    - "island loss for learning discriminative features in facial expression recognition"
    - "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "optimizing filter size in convolutional neural networks for facial action unit recognition"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-6 (induced by source paper "group emotion recognition using machine learning"):
  with members:
    - "emotion recognition in the wild using deep neural networks and bayesian classifiers"
    - "ssd single shot multibox detector"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-7 (induced by source paper "deep fusion an attention guided factorized bilinear pooling for audio video emotion recognition"):
  with members:
    - "attention based fully convolutional network for speech emotion recognition"
    - "temporal multimodal fusion for video emotion classification in the wild"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "factorized bilinear models for image recognition"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "the omg empathy dataset evaluating the impact of affective behavior in storytelling"):
  with members:
    - "multimodal sentiment analysis using hierarchical fusion with context modeling"
    - "the omg emotion behavior dataset"
    - "multimodal emotion recognition for one minute gradual emotion challenge"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "soundnet learning sound representations from unlabeled video"
    - "very deep convolutional networks for large scale image 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)