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 "metafusion controlled false negative reduction of minority classes in semantic segmentation"):
  Abstract: In semantic segmentation datasets, classes of high importance are oftentimes underrepresented, e.g., humans in street scenes. Neural networks are usually trained to reduce the overall number of errors, attaching identical loss to errors of all kinds. However, this is not necessarily aligned with human intuition. For instance, an overlooked pedestrian seems more severe than an incorrectly detected one. One possible remedy is to deploy different decision rules by introducing class priors which assigns larger weight to underrepresented classes. While reducing the false-negatives of the underrepresented class, at the same time this leads to a considerable increase of false-positive indications. In this work, we combine decision rules with methods for false-positive detection. We therefore fuse false-negative detection with uncertainty based false-positive meta classification. We present proof-of-concept results for CIFAR-10, and prove the efficiency of our method for the semantic segmentation of street scenes on the Cityscapes dataset based on predicted instances of the 'human' class. In the latter we employ an advanced false-positive detection method using uncertainty measures aggregated over instances. We thereby achieve improved trade-offs between false-negative and false-positive samples of the underrepresented classes.

Includes members:
  Member "uncertainty measures and prediction quality rating for the semantic segmentation of nested multi resolution street scene images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time dynamic estimates of the reliability of deep semantic segmentation networks")
  Member "time dynamic estimates of the reliability of deep semantic segmentation networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "detection of false positive and false negative samples in semantic segmentation")
  Member "the ethical dilemma when not setting up cost based decision rules in semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "strategy to increase the safety of a dnn based perception for had systems")
  Member "application of decision rules for handling class imbalance in semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforced active learning for image segmentation")
  Member "leveraging uncertainty estimates for predicting segmentation quality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hyperparameter free out of distribution detection using softmax of scaled cosine similarity")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "strategy to increase the safety of a dnn based perception for had systems")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)