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 "single image based head pose estimation with spherical parameterization and 3d morphing"):
  Abstract: Head pose estimation plays a vital role in various applications, e.g., driverassistance systems, human-computer interaction, virtual reality technology, and so on. We propose a novel geometry based algorithm for accurately estimating the head pose from a single 2D face image at a very low computational cost. Specifically, the rectangular coordinates of only four non-coplanar feature points from a predefined 3D facial model as well as the corresponding ones automatically/ manually extracted from a 2D face image are first normalized to exclude the effect of external factors (i.e., scale factor and translation parameters). Then, the four normalized 3D feature points are represented in spherical coordinates with reference to the uniquely determined sphere by themselves. Due to the spherical parameterization, the coordinates of feature points can then be morphed along all the three directions in the rectangular coordinates effectively. Finally, the rotation matrix indicating the head pose is obtained by minimizing the Euclidean distance between the normalized 2D feature points and the 2D re-projections of morphed 3D feature points. Comprehensive experimental results over two popular databases, i.e., Pointing'04 and Biwi Kinect, demonstrate that the proposed algorithm can estimate head poses with higher accuracy and lower run time than state-of-the-art geometry based methods. Even compared with start-of-the-art learning based methods or geometry based methods with additional depth information, our algorithm still produces comparable performance.

Includes members:
  Member "searching for mobilenetv3":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "fine grained head pose estimation without keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "touchless typing using head movement based gestures")
  Member "robust head pose estimation based on partially latent mixture of linear regressions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "laeo net revisiting people looking at each other in videos")
  Member "from depth data to head pose estimation a siamese approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a differential approach for gaze estimation")
  Member "owl and lizard patterns of head pose and eye pose in driver gaze classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mit advanced vehicle technology study large scale naturalistic driving study of driver behavior and interaction with automation")

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)