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 "the importance of metric learning for robotic vision open set recognition and active learning"):
  Abstract: State-of-the-art deep neural network recognition systems are designed for a static and closed world. It is usually assumed that the distribution at test time will be the same as the distribution during training. As a result, classifiers are forced to categorise observations into one out of a set of predefined semantic classes. Robotic problems are dynamic and open world; a robot will likely observe objects that are from outside of the training set distribution. Classifier outputs in robotic applications can lead to real-world robotic action and as such, a practical recognition system should not silently fail by confidently misclassifying novel observations. We show how a deep metric learning classification system can be applied to such open set recognition problems, allowing the classifier to label novel observations as unknown. Further to detecting novel examples, we propose an open set active learning approach that allows a robot to efficiently query a user about unknown observations. Our approach enables a robot to improve its understanding of the true distribution of data in the environment, from a small number of label queries. Experimental results show that our approach significantly outperforms comparable methods in both the open set recognition and active learning problems.

Includes members:
  Member "deep metric learning and image classification with nearest neighbour gaussian kernels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "opengan open set generative adversarial networks")
  Member "metric learning for novelty and anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi class novelty detection using mix up technique")
  Member "novelty detection with gan":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "image based ood detector principles on graph based input data in human action recognition")
  Member "generative adversarial active learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "baod budget aware object detection")
  Member "cost effective active learning for deep image classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "active and incremental learning with weak supervision")
  Member "learning how to active learn a deep reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforced active learning for image segmentation")
  Member "distance based confidence score for neural network classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relevant features based auxiliary cells for energy efficient detection of natural errors")
  Member "smart mining for deep metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "opengan open set generative adversarial networks")

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)