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 "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.

Includes members:
  Member "outlier exposure with confidence control for out of distribution detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "why should we combine training and post training methods for out of distribution detection")
  Member "reducing network agnostophobia":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalized odin detecting out of distribution image without learning from out of distribution data")
  Member "deep anomaly detection with outlier exposure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "synthesize then compare detecting failures and anomalies for semantic segmentation")
  Member "enhancing the reliability of out of distribution image detection in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "on calibration of modern neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "prototypical networks for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "a baseline for detecting misclassified and out of distribution examples in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "anomalous instance detection in deep learning a survey")

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.LG(Machine Learning)