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 "combining deep and depth deep learning and face depth maps for driver attention monitoring"):
  Abstract: Recently, deep learning approaches have achieved promising results in various fields of computer vision. In this paper, we investigate the combination of deep learning based methods and depth maps as input images to tackle the problem of driver attention monitoring. Moreover, we assume the concept of attention as Head Pose Estimation and Facial Landmark Detection tasks. Differently from other proposals in the literature, the proposed systems are able to work directly and based only on raw depth data. All presented methods are trained and tested on two new public datasets, namely Pandora and MotorMark, achieving state-of-art results and running with real time performance.

Includes members:
  Member "domain translation with conditional gans from depth to rgb face to face":
    also connected through other hyperedges:
      (none)
  Member "learning to generate facial depth maps":
    also connected through other hyperedges:
      (none)
  Member "from depth data to head pose estimation a siamese approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a differential approach for gaze estimation")
  Member "head detection with depth images in the wild":
    also connected through other hyperedges:
      (none)
  Member "deep head pose estimation from depth data for in car automotive applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "domain translation with conditional gans from depth to rgb face to face")
  Member "face from depth for head pose estimation on depth images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "domain translation with conditional gans from depth to rgb face to face")
  Member "poseidon face from depth for driver pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention aware fusion rgb d face recognition")
  Member "driver distraction detection and recognition using rgb d sensor":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "driver distraction identification with an ensemble of convolutional neural networks")

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.CV(Computer Vision and Pattern Recognition)