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: "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.

Connected through hyperedge HE-1 (induced by source paper "angular visual hardness"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "confidence regularized self training"
    - "compressive hyperspherical energy minimization"
    - "do imagenet classifiers generalize to imagenet"
    - "decoupled networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning towards minimum hyperspherical energy"
    - "not all samples are created equal deep learning with importance sampling"

Connected through hyperedge HE-2 (induced by source paper "efficient evaluation time uncertainty estimation by improved distillation"):
  with members:
    - "discrimination aware channel pruning for deep neural networks"
    - "large scale distributed neural network training through online distillation"
    - "dropout distillation for efficiently estimating model confidence"
    - "fixing variational bayes deterministic variational inference for bayesian neural networks"
    - "on calibration of modern neural networks"
    - "snapshot ensembles train 1 get m for free"
    - "selective classification for deep neural networks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-3 (induced by source paper "omnia faster r cnn detection in the wild through dataset merging and soft distillation"):
  with members:
    - "zero shot detection"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "cornernet detecting objects as paired keypoints"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "combining heterogeneously labeled datasets for training segmentation networks"
    - "open set domain adaptation by backpropagation"
    - "a two stream mutual attention network for semi supervised biomedical segmentation with noisy labels"
    - "deep visual domain adaptation a survey"

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.CV(Computer Vision and Pattern Recognition)