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 "holoface augmenting human to human interactions on hololens"):
  Abstract: We present HoloFace, an open-source framework for face alignment, head pose estimation and facial attribute retrieval for Microsoft HoloLens. HoloFace implements two state-of-the-art face alignment methods which can be used interchangeably: one running locally and one running on a remote backend. Head pose estimation is accomplished by fitting a deformable 3D model to the landmarks localized using face alignment. The head pose provides both the rotation of the head and a position in the world space. The parameters of the fitted 3D face model provide estimates of facial attributes such as mouth opening or smile. Together the above information can be used to augment the faces of people seen by the HoloLens user, and thus their interaction. Potential usage scenarios include facial recognition, emotion recognition, eye gaze tracking and many others. We demonstrate the capabilities of our framework by augmenting the faces of people seen through the HoloLens with various objects and animations.

Includes members:
  Member "deep alignment network a convolutional neural network for robust face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint face alignment and 3d face reconstruction with application to face recognition")
  Member "pose invariant face alignment with a single cnn":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust facial landmark detection via aggregation on geometrically manipulated faces")
  Member "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bats binary architecture search")
  Member "joint face detection and alignment using multitask cascaded convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "face alignment using k cluster regression forests with weighted splitting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "face alignment using a 3d deeply initialized ensemble of regression trees")
  Member "wider face a face detection benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generalizing face representation with unlabeled data")
  Member "growing regression forests by classification applications to object pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wing loss for robust facial landmark localisation with 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)