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: "isotropic maximization loss and entropic score fast accurate scalable unexposed turnkey and native neural networks out of distribution detection"
  Abstract: Current out-of-distribution detection (ODD) approaches require cumbersome procedures that add undesired side-effects to the solution. In this paper, we argue that the uncertainty in neural networks is mainly due to SoftMax loss anisotropy. Consequently, we propose an isotropic loss (IsoMax) and a decision score (Entropic Score) to significantly improve the ODD performance while keeping the overall solution fast, accurate, scalable, unexposed, turnkey, and native. Our experiments indeed showed that uncertainty is extremely reduced simply by replacing the SoftMax loss without relying on techniques such as adversarial training/validation, special-purpose data augmentation, outlier exposure, ensembles methods, Bayesian mechanisms, generative approaches, metric learning, or additional classifiers/regressions. The results also showed that our straightforward proposal overcomes ODIN, ACET, and is competitive against the Mahalanobis approach besides avoiding their undesired requirements and weaknesses. Since IsoMax loss works as a direct and transparent SoftMax loss drop-in replacement, these techniques may be used combined with our loss to increase the overall performance even more if their associated drawbacks are not a concern in a particular use case.

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)