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 "reducing overconfident errors outside the known distribution"):
  Abstract: Intuitively, unfamiliarity should lead to lack of confidence. In reality, current algorithms often make highly confident yet wrong predictions when faced with unexpected test samples from an unknown distribution different from training. Unlike domain adaptation methods, we cannot gather an "unexpected dataset" prior to test, and unlike novelty detection methods, the input examples have labels within the label domain, and a best-effort prediction is required. We compare a number of methods from related fields such as calibration and epistemic uncertainty modeling. Two proposed variants that reduce overconfident errors on an unknown novel distribution are also compared: (1) $\mathcal G$-distillation, training an ensemble of classifiers and then distilling into a single model using both labeled and unlabeled examples, and (2) NCR, reducing the confidence of a prediction based on the estimated novelty of the example. Experimentally, we investigate the overconfidence problem and evaluate methods by creating "familiar" and "novel" test splits, where "familiar" are identically distributed with training and "novel" are not. We discover that, surprisingly given its simplicity, calibrating using temperature scaling on only familiar data is the best single-model method for improving image confidence, followed by our proposed methods, then by other methods that may take unknown distributions into account. The top performing methods can also reduce confident errors, e.g. by 95\% in recognizing gender in demographic groups different from the training data.

Includes members:
  Member "generalizing across domains via cross gradient training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "enhancing the reliability of out of distribution image detection in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "data distillation towards omni supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "few shot few shot learning and the role of spatial attention")
  Member "deeper broader and artier domain generalization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "what uncertainties do we need in bayesian deep learning for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "heterogeneous face attribute estimation a deep multi task learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep multi task multi label cnn for effective facial attribute classification")
  Member "on the safety of machine learning cyber physical systems decision sciences and data products":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "training adversarial agents to exploit weaknesses in deep control policies")

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)