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 "zoomout spectral upsampling for efficient shape correspondence"):
  Abstract: We present a simple and efficient method for refining maps or correspondences by iterative upsampling in the spectral domain that can be implemented in a few lines of code. Our main observation is that high quality maps can be obtained even if the input correspondences are noisy or are encoded by a small number of coefficients in a spectral basis. We show how this approach can be used in conjunction with existing initialization techniques across a range of application scenarios, including symmetry detection, map refinement across complete shapes, non-rigid partial shape matching and function transfer. In each application we demonstrate an improvement with respect to both the quality of the results and the computational speed compared to the best competing methods, with up to two orders of magnitude speed-up in some applications. We also demonstrate that our method is both robust to noisy input and is scalable with respect to shape complexity. Finally, we present a theoretical justification for our approach, shedding light on structural properties of functional maps.

Includes members:
  Member "continuous and orientation preserving correspondences via functional maps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment")
  Member "unsupervised deep learning for structured shape matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "instant recovery of shape from spectrum via latent space connections")
  Member "fast and accurate intrinsic symmetry detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning based real time detection of intrinsic reflectional symmetry")
  Member "farm functional automatic registration method for 3d human bodies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the whole is greater than the sum of its nonrigid parts")
  Member "reversible harmonic maps between discrete surfaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "prnet self supervised learning for partial to partial registration")
  Member "product manifold filter non rigid shape correspondence via kernel density estimation in the product space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment")
  Member "localized manifold harmonics for spectral shape analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "correspondence free region localization for partial shape similarity via hamiltonian spectrum alignment")
  Member "point wise map recovery and refinement from functional correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "smooth shells multi scale shape registration with functional maps")

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.GR(Graphics)