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: "the omg emotion behavior dataset"
  Abstract: This paper is the basis paper for the accepted IJCNN challenge One-Minute Gradual-Emotion Recognition (OMG-Emotion) by which we hope to foster long-emotion classification using neural models for the benefit of the IJCNN community. The proposed corpus has as the novelty the data collection and annotation strategy based on emotion expressions which evolve over time into a specific context. Different from other corpora, we propose a novel multimodal corpus for emotion expression recognition, which uses gradual annotations with a focus on contextual emotion expressions. Our dataset was collected from Youtube videos using a specific search strategy based on restricted keywords and filtering which guaranteed that the data follow a gradual emotion expression transition, i.e. emotion expressions evolve over time in a natural and continuous fashion. We also provide an experimental protocol and a series of unimodal baseline experiments which can be used to evaluate deep and recurrent neural models in a fair and standard manner.

Connected through hyperedge HE-1 (induced by source paper "multitask emotion recognition with incomplete labels"):
  with members:
    - "analysing affective behavior in the first abaw 2020 competition"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "face behavior a la carte expressions affect and action units in a single network"
    - "mimamo net integrating micro and macro motion for video emotion recognition"
    - "model distillation with knowledge transfer from face classification to alignment and verification"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "emotionet challenge recognition of facial expressions of emotion in the wild"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-2 (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"
    - "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"
    - "a multi task learning generation framework valence arousal action units primary expressions"

Connected through hyperedge HE-3 (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"
    - "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"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "geometry guided adversarial facial expression synthesis"

Connected through hyperedge HE-4 (induced by source paper "modeling emotion in complex stories the stanford emotional narratives dataset"):
  with members:
    - "applying probabilistic programming to affective computing"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "sewa db a rich database for audio visual emotion and sentiment research in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "attending to emotional narratives"
    - "a multimodal lstm for predicting listener empathic responses over time"
    - "multimodal generative models for scalable weakly supervised learning"
    - "pomegranate fast and flexible probabilistic modeling in python"

Connected through hyperedge HE-5 (induced by source paper "emobed strengthening monomodal emotion recognition via training with crossmodal emotion embeddings"):
  with members:
    - "dynamic difficulty awareness training for continuous emotion prediction"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "adversarial training in affective computing and sentiment analysis recent advances and perspectives"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "soundnet learning sound representations from unlabeled video"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "emotion recognition system from speech and visual information based on convolutional neural networks"):
  with members:
    - "realistic speech driven facial animation with gans"
    - "deep facial expression recognition a survey"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "dexpression deep convolutional neural network for expression recognition"
    - "emonets multimodal deep learning approaches for emotion recognition in video"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "kt speech crawler automatic dataset construction for speech recognition from youtube videos"):
  with members:
    - "mining fine grained opinions on closed captions of youtube videos with an attention rnn"
    - "the kinetics human action video dataset"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "first pass large vocabulary continuous speech recognition using bi directional recurrent dnns"
    - "deep speech scaling up end to end speech recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "mimamo net integrating micro and macro motion for video emotion recognition"):
  with members:
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "a deep network for arousal valence emotion prediction with acoustic visual cues"
    - "multimodal emotion recognition for one minute gradual emotion challenge"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"

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.HC(Human-Computer Interaction)