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 "target driven visual navigation exploiting object relationships"):
  Abstract: Recently target driven visual navigation strategies have gained a lot of popularity in the computer vision and reinforcement learning community. Unfortunately, most of the current research tends to incorporate sensory input into a reward-based learning approach, with the hope that a robot can implicitly learn its optimal actions through recursive trials. These methods seldom generalize across domains as they fail to exploit natural environment object relationships. We present Memory-utilized Joint hierarchical Object Learning for Navigation in Indoor Rooms (MJOLNIR), a target-driven visual navigation algorithm, which considers the inherent relationship between "target" objects, along with the more salient "parent" objects occurring in its surrounding. Extensive experiments conducted across multiple environment settings show $\approx \textbf{30 %}$ improvement over the existing state-of-the-art navigation methods in terms of the success rate. We also show that our model learns to converge much faster than other algorithms. We will make our code publicly available for use in the scientific community.

Includes members:
  Member "ok vqa a visual question answering benchmark requiring external knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "vqa lol visual question answering under the lens of logic")
  Member "deduce diverse scene detection methods in unseen challenging environments":
    also connected through other hyperedges:
      (none)
  Member "benchmarking classic and learned navigation in complex 3d environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an exploration of embodied visual exploration")
  Member "looking at the right stuff guided semantic gaze for autonomous driving":
    also connected through other hyperedges:
      (none)
  Member "to learn or not to learn analyzing the role of learning for navigation in virtual environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "audio visual embodied navigation")
  Member "on evaluation of embodied navigation agents":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an exploration of embodied visual exploration")
  Member "subgoal discovery for hierarchical dialogue policy learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hierarchical reinforcement learning for open domain dialog")
  Member "learning to learn how to learn self adaptive visual navigation using meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "audio visual embodied navigation")

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)