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 "faceposenet making a case for landmark free face alignment"):
  Abstract: We show how a simple convolutional neural network (CNN) can be trained to accurately and robustly regress 6 degrees of freedom (6DoF) 3D head pose, directly from image intensities. We further explain how this FacePoseNet (FPN) can be used to align faces in 2D and 3D as an alternative to explicit facial landmark detection for these tasks. We claim that in many cases the standard means of measuring landmark detector accuracy can be misleading when comparing different face alignments. Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method. Our results show that (a) better landmark detection accuracy measured on the 300W benchmark does not necessarily imply better face recognition accuracy. (b) Our FPN provides superior 2D and 3D face alignment on both benchmarks. Finally, (c), FPN aligns faces at a small fraction of the computational cost of comparably accurate landmark detectors. For many purposes, FPN is thus a far faster and far more accurate face alignment method than using facial landmark detectors.

Includes members:
  Member "l2 constrained softmax loss for discriminative face verification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalizing face representation with unlabeled data")
  Member "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning for face recognition a critical analysis")
  Member "do we really need to collect millions of faces for effective face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalizing face representation with unlabeled data")
  Member "marr revisited 2d 3d alignment via surface normal prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep iterative surface normal estimation")
  Member "a multi scale cascade fully convolutional network face detector":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning structure via consensus for face segmentation and parsing")
  Member "multi directional multi level dual cross patterns for robust face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ldop local directional order pattern for robust face retrieval")
  Member "regressing robust and discriminative 3d morphable models with a very deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "face hallucination with finishing touches")
  Member "learning detailed face reconstruction from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the whole is greater than the sum of its nonrigid parts")

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)