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: "fast optimal transport averaging of neuroimaging data"
  Abstract: Knowing how the Human brain is anatomically and functionally organized at the level of a group of healthy individuals or patients is the primary goal of neuroimaging research. Yet computing an average of brain imaging data defined over a voxel grid or a triangulation remains a challenge. Data are large, the geometry of the brain is complex and the between subjects variability leads to spatially or temporally non-overlapping effects of interest. To address the problem of variability, data are commonly smoothed before group linear averaging. In this work we build on ideas originally introduced by Kantorovich to propose a new algorithm that can average efficiently non-normalized data defined over arbitrary discrete domains using transportation metrics. We show how Kantorovich means can be linked to Wasserstein barycenters in order to take advantage of an entropic smoothing approach. It leads to a smooth convex optimization problem and an algorithm with strong convergence guarantees. We illustrate the versatility of this tool and its empirical behavior on functional neuroimaging data, functional MRI and magnetoencephalography (MEG) source estimates, defined on voxel grids and triangulations of the folded cortical surface.

Connected through hyperedge HE-1 (induced by source paper "adversarial computation of optimal transport maps"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "spectral normalization for generative adversarial networks"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "improving gans using optimal transport"
    - "on convergence and stability of gans"
    - "solving approximate wasserstein gans to stationarity"
    - "guiding the one to one mapping in cyclegan via optimal transport"
    - "pacgan the power of two samples in generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "faster algorithms for the geometric transportation problem"):
  with members:
    - "dynamic planar voronoi diagrams for general distance functions and their algorithmic applications"
    - "parallel algorithms for geometric graph problems"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-3 (induced by source paper "blind source separation with optimal transport non negative matrix factorization"):
  with members:
    - "learning with a wasserstein loss"

Connected through hyperedge HE-4 (induced by source paper "near linear time approximation algorithms for optimal transport via sinkhorn iteration"):
  with members:
    - "much faster algorithms for matrix scaling"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "parallel algorithms for geometric graph problems"

Connected through hyperedge HE-5 (induced by source paper "convex histogram based joint image segmentation with regularized optimal transport cost"):
  with members:
    - "convex color image segmentation with optimal transport distances"
    - "an inertial forward backward algorithm for monotone inclusions"
    - "learning with a wasserstein loss"
    - "globally optimal joint image segmentation and shape matching based on wasserstein modes"
    - "imaging with kantorovich rubinstein discrepancy"
    - "regularized discrete optimal transport"
    - "total variation minimization and graph cuts for moving objects segmentation"

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.CV(Computer Vision and Pattern Recognition)