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: "adversarial computation of optimal transport maps"
  Abstract: Computing optimal transport maps between high-dimensional and continuous distributions is a challenging problem in optimal transport (OT). Generative adversarial networks (GANs) are powerful generative models which have been successfully applied to learn maps across high-dimensional domains. However, little is known about the nature of the map learned with a GAN objective. To address this problem, we propose a generative adversarial model in which the discriminator's objective is the $2$-Wasserstein metric. We show that during training, our generator follows the $W_2$-geodesic between the initial and the target distributions. As a consequence, it reproduces an optimal map at the end of training. We validate our approach empirically in both low-dimensional and high-dimensional continuous settings, and show that it outperforms prior methods on image data.

Connected through hyperedge HE-1 (induced by source paper "large scale optimal transport via adversarial training with cycle consistency"):
  with members:
    - "on scalable and efficient computation of large scale optimal transport"
    - "optimal transport mapping via input convex neural networks"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "guiding the one to one mapping in cyclegan via optimal transport"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "fliptest fairness testing via optimal transport"):
  with members:
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "fairness under composition"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "approximating optimal transport with linear programs"
    - "automated test generation to detect individual discrimination in ai models"
    - "hunting for discriminatory proxies in linear regression models"
    - "an empirical study of rich subgroup fairness for machine learning"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"

Connected through hyperedge HE-3 (induced by source paper "potential flow generator with l_2 optimal transport regularity for generative models"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "improving gans using optimal transport"
    - "neural ordinary differential equations"
    - "generative modeling using the sliced wasserstein distance"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "optimal transport mapping via input convex neural networks"):
  with members:
    - "on scalable and efficient computation of large scale optimal transport"
    - "subspace detours building transport plans that are optimal on subspace projections"
    - "mode collapse and regularity of optimal transportation maps"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "generative modeling using the sliced wasserstein distance"
    - "pacgan the power of two samples in generative adversarial networks"
    - "a geometric view of optimal transportation and generative model"
    - "mmd gan towards deeper understanding of moment matching network"

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.LG(Machine Learning)