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 "active learning for uav based semantic mapping"):
  Abstract: Unmanned aerial vehicles combined with computer vision systems, such as convolutional neural networks, offer a flexible and affordable solution for terrain monitoring, mapping, and detection tasks. However, a key challenge remains the collection and annotation of training data for the given sensors, application, and mission. We introduce an informative path planning system that incorporates novelty estimation into its objective function, based on research for uncertainty estimation in deep learning. The system is designed for data collection to reduce both the number of flights and of annotated images. We evaluate the approach on real world terrain mapping data and show significantly smaller collected training dataset compared to standard lawnmower data collection techniques.

Includes members:
  Member "detecting the unexpected via image resynthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using depth for pixel wise detection of adversarial attacks in crowd counting")
  Member "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bayesian spatial kernel smoothing for scalabledense semantic mapping")
  Member "the fishyscapes benchmark measuring blind spots in semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learnable bernoulli dropout for bayesian deep learning")
  Member "modular sensor fusion for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self supervised visual terrain classification from unsupervised acoustic feature learning")
  Member "deep k nearest neighbors towards confident interpretable and robust deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "generative probabilistic novelty detection with adversarial autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "memory augmented generative adversarial networks for anomaly detection")
  Member "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "elkppnet an edge aware neural network with large kernel pyramid pooling for learning discriminative features in semantic segmentation")
  Member "cost effective active learning for deep image classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "active and incremental learning with weak supervision")

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.RO(Robotics)