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: "identifying stable patterns over time for emotion recognition from eeg"
  Abstract: In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. To validate the efficiency of the machine learning algorithms used in this study, we systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset for this study. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotion than negative one in beta and gamma bands; the neural patterns of neutral emotion have higher alpha responses at parietal and occipital sites; and for negative emotion, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition system shows that the neural patterns are relatively stable within and between sessions.

Connected through hyperedge HE-1 (induced by source paper "jointly aligning and predicting continuous emotion annotations"):
  with members:
    - "trainable time warping aligning time series in the continuous time domain"
    - "the priori emotion dataset linking mood to emotion detected in the wild"
    - "capturing long term temporal dependencies with convolutional networks for continuous emotion recognition"
    - "progressive neural networks for transfer learning in emotion recognition"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "learning representations of emotional speech with deep convolutional generative adversarial networks"
    - "emotion recognition by body movement representation on the manifold of symmetric positive definite matrices"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"

Connected through hyperedge HE-2 (induced by source paper "on assessing driver awareness of situational criticalities multi modal bio sensing and vision based analysis evaluations and insights"):
  with members:
    - "utilizing deep learning towards multi modal bio sensing and vision based affective computing"
    - "attention monitoring and hazard assessment with bio sensing and vision empirical analysis utilizing cnns on the kitti dataset"
    - "driver hand localization and grasp analysis a vision based real time approach"
    - "multi modal approach for affective computing"
    - "imitating driver behavior with generative adversarial networks"
    - "detecting unexpected obstacles for self driving cars fusing deep learning and geometric modeling"
    - "an empirical evaluation of deep learning on highway driving"
    - "learning representations from eeg with deep recurrent convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "utilizing deep learning towards multi modal bio sensing and vision based affective computing"):
  with members:
    - "driver hand localization and grasp analysis a vision based real time approach"
    - "video salient object detection via fully convolutional networks"
    - "an affordable bio sensing and activity tagging platform for hci research"
    - "multi modal approach for affective computing"
    - "you only look once unified real time object detection"
    - "learning representations from eeg with deep recurrent convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "probabilistic feature selection and classification vector machine"):
  with members:
    - "incremental sparse bayesian ordinal regression"
    - "online feature selection with group structure analysis"
    - "scalable and accurate online feature selection for big data"
    - "large scale online feature selection for ultra high dimensional sparse data"
    - "variational relevance vector machines"
    - "transductive rademacher complexity and its applications"

Connected through hyperedge HE-5 (induced by source paper "multimodal emotion recognition using deep canonical correlation analysis"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "mine mutual information neural estimation"
    - "multimodal machine learning a survey and taxonomy"

Connected through hyperedge HE-6 (induced by source paper "dasps a database for anxious states based on a psychological stimulation"):
  with members:
    - "unsupervised learning in reservoir computing for eeg based emotion recognition"

Connected through hyperedge HE-7 (induced by source paper "alzheimer s disease diagnosis based on cognitive methods in virtual environments and emotions analysis"):
  with members:
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "modeling spatial and temporal cues for multi label facial action unit detection"
    - "face expression recognition and analysis the state of the art"

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)