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 "deep cnn based multi task learning for open set recognition"):
  Abstract: We propose a novel deep convolutional neural network (CNN) based multi-task learning approach for open-set visual recognition. We combine a classifier network and a decoder network with a shared feature extractor network within a multi-task learning framework. We show that this approach results in better open-set recognition accuracy. In our approach, reconstruction errors from the decoder network are utilized for open-set rejection. In addition, we model the tail of the reconstruction error distribution from the known classes using the statistical Extreme Value Theory to improve the overall performance. Experiments on multiple image classification datasets are performed and it is shown that this method can perform significantly better than many competitive open set recognition algorithms available in the literature. The code will be made available at: this http URL.

Includes members:
  Member "learning deep features for one class classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning achieves perfect anomaly detection on 108 308 retinal images including unlearned diseases")
  Member "open category detection with pac guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "utilizing network properties to detect erroneous inputs")
  Member "deep anomaly detection using geometric transformations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "and autoregressive novelty detectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection")
  Member "learning from noisy web data with category level supervision":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi class classification from noisy similarity labeled data")
  Member "classification reconstruction learning for open set recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recent advances in open set recognition a survey")
  Member "the extreme value machine":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "curriculum labeling self paced pseudo labeling for semi supervised learning")

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)