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 "habitat a platform for embodied ai research"):
  Abstract: We present Habitat, a new platform for research in embodied artificial intelligence (AI). Habitat enables training embodied agents (virtual robots) in highly efficient photorealistic 3D simulation, before transferring the learned skills to reality. #R##N#Specifically, Habitat consists of the following: 1. Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, multiple sensors, and generic 3D dataset handling (with built-in support for SUNCG, Matterport3D, Gibson datasets). Habitat-Sim is fast -- when rendering a scene from the Matterport3D dataset, Habitat-Sim achieves several thousand frames per second (fps) running single-threaded, and can reach over 10,000 fps multi-process on a single GPU, which is orders of magnitude faster than the closest simulator. 2. Habitat-API: a modular high-level library for end-to-end development of embodied AI algorithms -- defining embodied AI tasks (e.g. navigation, instruction following, question answering), configuring and training embodied agents (via imitation or reinforcement learning, or via classic SLAM), and benchmarking using standard metrics. #R##N#These large-scale engineering contributions enable us to answer scientific questions requiring experiments that were till now impracticable or `merely' impractical. Specifically, in the context of point-goal navigation (1) we revisit the comparison between learning and SLAM approaches from two recent works and find evidence for the opposite conclusion -- that learning outperforms SLAM, if scaled to total experience far surpassing that of previous investigations, and (2) we conduct the first cross-dataset generalization experiments {train, test} x {Matterport3D, Gibson} for multiple sensors {blind, RGB, RGBD, D} and find that only agents with depth (D) sensors generalize across datasets. We hope that our open-source platform and these findings will advance research in embodied AI.

Includes members:
  Member "the replica dataset a digital replica of indoor spaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an exploration of embodied visual exploration")
  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 "pyrobot an open source robotics framework for research and benchmarking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "audio visual embodied navigation")
  Member "learning agile and dynamic motor skills for legged robots":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning")
  Member "to learn or not to learn analyzing the role of learning for navigation in virtual environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "target driven visual navigation exploiting object relationships")
  Member "virtualhome simulating household activities via programs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "arena a general evaluation platform and building toolkit for multi agent intelligence")
  Member "mid level visual representations improve generalization and sample efficiency for learning active tasks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaling and benchmarking self supervised visual representation learning")
  Member "gibson env real world perception for embodied agents":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an exploration of embodied visual exploration")

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)