human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on assessing driver awareness of situational criticalities multi modal bio sensing and vision based analysis evaluations and insights"):
  Abstract: Automobiles for our roadways are increasingly utilizing advanced driver assistance systems. These changes require us to develop novel perception systems not only for accurately understanding the situations and context of these vehicles, but also to understand the awareness of the driver in differentiating between safe and critical situations. The research presented in this paper is focused on this specific problem. Even after the development of wearable and compact multi-modal bio-sensing systems in recent years, their application in driver awareness context has been scarcely explored. The capability of simultaneously recording different kinds of bio-sensing data in addition to traditionally used computer vision systems provide exciting opportunities to explore the limitations of these modalities. In this work, we explore the applications of three different bio-sensing modalities namely electroencephalogram (EEG), photoplethysmogram (PPG) and galvanic skin response (GSR) along with a camera-based vision system in driver awareness context. We assess the information from these sensors independently and together using both signal processing and deep learning tools. We show that our methods outperform previously reported studies in the context of monitoring driver awareness and detecting hazardous/non-hazardous situations for short time scales of two-seconds. We verify our methods by collecting user data on twelve subjects for two real-world driving datasets among which one is publicly available (KITTI dataset) and one that we collect ourselves (LISA dataset) with the vehicle being driven in autonomous mode. This work presents an exhaustive evaluation of multiple sensor modalities on two different datasets for attention monitoring and hazardous events classification.

Includes members:
  Member "utilizing deep learning towards multi modal bio sensing and vision based affective computing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "end to end facial and physiological model for affective computing and applications")
  Member "attention monitoring and hazard assessment with bio sensing and vision empirical analysis utilizing cnns on the kitti dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "looking at the driver rider in autonomous vehicles to predict take over readiness")
  Member "driver hand localization and grasp analysis a vision based real time approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "utilizing deep learning towards multi modal bio sensing and vision based affective computing")
  Member "multi modal approach for affective computing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "utilizing deep learning towards multi modal bio sensing and vision based affective computing")
  Member "imitating driver behavior with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial imitation learning with neural networks global optimality and convergence rate")
  Member "detecting unexpected obstacles for self driving cars fusing deep learning and geometric modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "real time fusion network for rgb d semantic segmentation incorporating unexpected obstacle detection for road driving images")
  Member "identifying stable patterns over time for emotion recognition from eeg":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "jointly aligning and predicting continuous emotion annotations")
  Member "an empirical evaluation of deep learning on highway driving":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "semi local 3d lane detection and uncertainty estimation")

Question: Please tell me which class the source paper 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)