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: "driver hand localization and grasp analysis a vision based real time approach"
  Abstract: Extracting hand regions and their grasp information from images robustly in real-time is critical for occupants' safety and in-vehicular infotainment applications. It must however, be noted that naturalistic driving scenes suffer from rapidly changing illumination and occlusion. This is aggravated by the fact that hands are highly deformable objects, and change in appearance frequently. This work addresses the task of accurately localizing driver hands and classifying the grasp state of each hand. We use a fast ConvNet to first detect likely hand regions. Next, a pixel-based skin classifier that takes into account the global illumination changes is used to refine the hand detections and remove false positives. This step generates a pixel-level mask for each hand. Finally, we study each such masked regions and detect if the driver is grasping the wheel, or in some cases a mobile phone. Through evaluation we demonstrate that our method can outperform state-of-the-art pixel based hand detectors, while running faster (at 35 fps) than other deep ConvNet based frameworks even for grasp analysis. Hand mask cues are shown to be crucial when analyzing a set of driver hand gestures (wheel/mobile phone grasp and no-grasp) in naturalistic driving settings. The proposed detection and localization pipeline hence can act as a general framework for real-time hand detection and gesture classification.

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"
    - "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"
    - "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:
    - "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"
    - "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:
    - "multi modal approach for affective computing"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "egocentric hand detection via dynamic region growing"):
  with members:
    - "joint hand detection and rotation estimation by using cnn"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "a survey on recent advances of computer vision algorithms for egocentric video"
    - "the evolution of first person vision methods a survey"
    - "an egocentric look at video photographer identity"

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)