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 "are out of distribution detection methods effective on large scale datasets"):
  Abstract: Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, deployed classifiers often require the ability to recognize inputs from outside the training set as unknowns. This problem has been studied under multiple paradigms including out-of-distribution detection and open set recognition. For convolutional neural networks, there have been two major approaches: 1) inference methods to separate knowns from unknowns and 2) feature space regularization strategies to improve model robustness to outlier inputs. There has been little effort to explore the relationship between the two approaches and directly compare performance on anything other than small-scale datasets that have at most 100 categories. Using ImageNet-1K and Places-434, we identify novel combinations of regularization and specialized inference methods that perform best across multiple outlier detection problems of increasing difficulty level. We found that input perturbation and temperature scaling yield the best performance on large scale datasets regardless of the feature space regularization strategy. Improving the feature space by regularizing against a background class can be helpful if an appropriate background class can be found, but this is impractical for large scale image classification datasets.

Includes members:
  Member "continual lifelong learning with neural networks a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "deep cnn based multi task learning for open set recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention aware age agnostic visual place recognition")
  Member "remind your neural network to prevent catastrophic forgetting":
    also connected through other hyperedges:
      (none)
  Member "benchmarking neural network robustness to common corruptions and perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "reducing network agnostophobia":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalized odin detecting out of distribution image without learning from out of distribution data")
  Member "out of distribution detection using an ensemble of self supervised leave out classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "memory efficient experience replay for streaming learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "remind your neural network to prevent catastrophic forgetting")

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)