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: "multi modal approach for affective computing"
  Abstract: Throughout the past decade, many studies have classified human emotions using only a single sensing modality such as face video, electroencephalogram (EEG), electrocardiogram (ECG), galvanic skin response (GSR), etc. The results of these studies are constrained by the limitations of these modalities such as the absence of physiological biomarkers in the face-video analysis, poor spatial resolution in EEG, poor temporal resolution of the GSR etc. Scant research has been conducted to compare the merits of these modalities and understand how to best use them individually and jointly. Using multi-modal AMIGOS dataset, this study compares the performance of human emotion classification using multiple computational approaches applied to face videos and various bio-sensing modalities. Using a novel method for compensating physiological baseline we show an increase in the classification accuracy of various approaches that we use. Finally, we present a multi-modal emotion-classification approach in the domain of affective computing research.

Connected through hyperedge HE-1 (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"
    - "imitating driver behavior with generative adversarial networks"
    - "detecting unexpected obstacles for self driving cars fusing deep learning and geometric modeling"
    - "identifying stable patterns over time for emotion recognition from eeg"
    - "an empirical evaluation of deep learning on highway driving"
    - "learning representations from eeg with deep recurrent convolutional neural networks"

Connected through hyperedge HE-2 (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"
    - "identifying stable patterns over time for emotion recognition from eeg"
    - "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-3 (induced by source paper "attention monitoring and hazard assessment with bio sensing and vision empirical analysis utilizing cnns on the kitti dataset"):
  with members:
    - "driver hand localization and grasp analysis a vision based real time approach"
    - "looking at the driver rider in autonomous vehicles to predict take over readiness"
    - "imitating driver behavior with generative adversarial networks"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "cnn features off the shelf an astounding baseline for 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.HC(Human-Computer Interaction)