human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "matching shapes using the current distance"
  Abstract: The {current distance} was introduced by Vaillant and Glaunes as a way of comparing shapes (point sets, curves, surfaces) without having to rely on computing correspondences between features in each shape. This distance measure is defined by viewing a shape as a linear operator on a k-form field, and constructing a (dual) norm on the space of shapes. As formulated, it takes O(nm) time to compute the current distance between two shapes of size n and m, and there are no known algorithms to compute the current distance between shapes minimized under transformation groups. In this paper, we provide the first algorithmic analysis of the current distance. Our main results are (i) a method for computing the approximate current distance between two shapes in near-linear time, (ii) a coreset construction that allows us to approximate the current norm of a shape using a constant-sized sample, and (iii) an approximation algorithm for computing the current distance between two d-dimensional shapes under rigid transformations (rotations and translations). #R##N#An interesting aspect of our work is that we can compute the current distance between curves, surfaces, and higher-order manifolds via a simple reduction to instances of weighted point sets, thus obviating the need for different kinds of algorithms for different kinds of shapes.

Connected through hyperedge HE-1 (induced by source paper "a gentle introduction to the kernel distance"):
  with members:
    - "spatially aware comparison and consensus for clusterings"

Connected through hyperedge HE-2 (induced by source paper "spatially aware comparison and consensus for clusterings"):
  with members:
    - "objective criteria for the evaluation of clustering methods"

Question: Please tell me which class the center node 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.CG(Computational Geometry)