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 "model free consensus maximization for non rigid shapes"):
  Abstract: Many computer vision methods use consensus maximization to relate measurements containing outliers with the correct transformation model. In the context of rigid shapes, this is typically done using Random Sampling and Consensus (RANSAC) by estimating an analytical model that agrees with the largest number of measurements (inliers). However, small parameter models may not be always available. In this paper, we formulate the model-free consensus maximization as an Integer Program in a graph using `rules' on measurements. We then provide a method to solve it optimally using the Branch and Bound (BnB) paradigm. We focus its application on non-rigid shapes, where we apply the method to remove outlier 3D correspondences and achieve performance superior to the state of the art. Our method works with outlier ratio as high as 80\%. We further derive a similar formulation for 3D template to image matching, achieving similar or better performance compared to the state of the art.

Includes members:
  Member "robust fitting in computer vision easy or hard":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "teaser fast and certifiable point cloud registration")
  Member "deep functional maps structured prediction for dense shape correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep graph matching consensus")
  Member "building statistical shape spaces for 3d human modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "calvis chest waist and pelvis circumference from 3d human body meshes as ground truth for deep learning")
  Member "product manifold filter non rigid shape correspondence via kernel density estimation in the product space":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment")
  Member "a combinatorial solution to non rigid 3d shape to image matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment")
  Member "volumedeform real time volumetric non rigid reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepcap monocular human performance capture using weak supervision")
  Member "mlpnp a real time maximum likelihood solution to the perspective n point problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "single stage 6d object pose estimation")
  Member "template based monocular 3d shape recovery using laplacian meshes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "shape reconstruction by learning differentiable surface representations")

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)