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 "hyperparameter free out of distribution detection using softmax of scaled cosine similarity"):
  Abstract: The ability to detect out-of-distribution (OOD) samples is vital to secure the reliability of deep neural networks in real-world applications. Considering the nature of OOD samples, detection methods should not have hyperparameters that need to be tuned depending on incoming OOD samples. However, most of the recently proposed methods do not meet this requirement, leading to compromised performance in real-world applications. In this paper, we propose a simple, hyperparameter-free method based on softmax of scaled cosine similarity. It resembles the approach employed by modern metric learning methods, but it differs in details; the differences are essential to achieve high detection performance. We show through experiments that our method outperforms the existing methods on the evaluation test recently proposed by Shafaei et al., which takes the above issue of hyperparameter dependency into account. We also show that it achieves at least comparable performance to other methods on the conventional test, where their hyperparameters are chosen using explicit OOD samples. Furthermore, it is computationally more efficient than most of the previous methods, since it needs only a single forward pass.

Includes members:
  Member "using pre training can improve model robustness and uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial distributional training for robust deep learning")
  Member "lightweight probabilistic deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving")
  Member "leveraging uncertainty estimates for predicting segmentation quality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "time dynamic estimates of the reliability of deep semantic segmentation networks")
  Member "confidence scoring using whitebox meta models with linear classifier probes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "calibration of deep probabilistic models with decoupled bayesian neural networks")
  Member "arcface additive angular margin loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "out of distribution detection using an ensemble of self supervised leave out classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "deep anomaly detection with outlier exposure":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "accurate uncertainties for deep learning using calibrated regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a financial service chatbot based on deep bidirectional transformers")

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.CV(Computer Vision and Pattern Recognition)