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 "capsule network performance with autonomous navigation"):
  Abstract: Capsule Networks (CapsNets) have been proposed as an alternative to Convolutional Neural Networks (CNNs). This paper showcases how CapsNets are more capable than CNNs for autonomous agent exploration of realistic scenarios. In real world navigation, rewards external to agents may be rare. In turn, reinforcement learning algorithms can struggle to form meaningful policy functions. This paper's approach Capsules Exploration Module (Caps-EM) pairs a CapsNets architecture with an Advantage Actor Critic algorithm. Other approaches for navigating sparse environments require intrinsic reward generators, such as the Intrinsic Curiosity Module (ICM) and Augmented Curiosity Modules (ACM). Caps-EM uses a more compact architecture without need for intrinsic rewards. Tested using ViZDoom, the Caps-EM uses 44% and 83% fewer trainable network parameters than the ICM and Depth-Augmented Curiosity Module (D-ACM), respectively, for 1141% and 437% average time improvement over the ICM and D-ACM, respectively, for converging to a policy function across "My Way Home" scenarios.

Includes members:
  Member "capsule neural network based height classification using low cost automotive ultrasonic sensors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "enabling highly efficient capsule networks processing through a pim based architecture design")
  Member "advanced capsule networks via context awareness":
    also connected through other hyperedges:
      (none)
  Member "deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards crisp ml q a machine learning process model with quality assurance methodology")
  Member "novel deep learning model for traffic sign detection using capsule networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "examining the benefits of capsule neural networks")
  Member "deep reinforcement learning using capsules in advanced game environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "examining the benefits of capsule neural networks")
  Member "simultaneous localization and mapping with depth prediction using capsule networks for uavs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tcdcaps visual tracking via cascaded dense capsules")
  Member "state representation learning with recurrent capsule networks":
    also connected through other hyperedges:
      (none)
  Member "scalable trust region method for deep reinforcement learning using kronecker factored approximation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")

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.LG(Machine Learning)