human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "pyrobot an open source robotics framework for research and benchmarking"
  Abstract: This paper introduces PyRobot, an open-source robotics framework for research and benchmarking. PyRobot is a light-weight, high-level interface on top of ROS that provides a consistent set of hardware independent mid-level APIs to control different robots. PyRobot abstracts away details about low-level controllers and inter-process communication, and allows non-robotics researchers (ML, CV researchers) to focus on building high-level AI applications. PyRobot aims to provide a research ecosystem with convenient access to robotics datasets, algorithm implementations and models that can be used to quickly create a state-of-the-art baseline. We believe PyRobot, when paired up with low-cost robot platforms such as LoCoBot, will reduce the entry barrier into robotics, and democratize robotics. PyRobot is open-source, and can be accessed via this https URL.

Connected through hyperedge HE-1 (induced by source paper "audio visual embodied navigation"):
  with members:
    - "two body problem collaborative visual task completion"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "do autonomous agents benefit from hearing"
    - "the replica dataset a digital replica of indoor spaces"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "co separating sounds of visual objects"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "interactive gibson benchmark for interactive navigation in cluttered environments"

Connected through hyperedge HE-2 (induced by source paper "offworld gym open access physical robotics environment for real world reinforcement learning benchmark and research"):
  with members:
    - "replab a reproducible low cost arm benchmark platform for robotic learning"
    - "learning latent state representation for speeding up exploration"
    - "go explore a new approach for hard exploration problems"
    - "pyrep bringing v rep to deep robot learning"
    - "self supervised exploration via disagreement"
    - "provably efficient imitation learning from observation alone"
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "challenges of real world reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "habitat a platform for embodied ai research"):
  with members:
    - "the replica dataset a digital replica of indoor spaces"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "learning agile and dynamic motor skills for legged robots"
    - "to learn or not to learn analyzing the role of learning for navigation in virtual environments"
    - "virtualhome simulating household activities via programs"
    - "mid level visual representations improve generalization and sample efficiency for learning active tasks"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"

Connected through hyperedge HE-4 (induced by source paper "learning navigation subroutines from egocentric videos"):
  with members:
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "imitating latent policies from observation"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "visual memory for robust path following"
    - "on evaluation of embodied navigation agents"
    - "sfv reinforcement learning of physical skills from videos"
    - "diversity is all you need learning skills without a reward function"

Connected through hyperedge HE-5 (induced by source paper "are we making real progress in simulated environments measuring the sim2real gap in embodied visual navigation"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "decentralized distributed ppo solving pointgoal navigation"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "learning agile and dynamic motor skills for legged robots"
    - "to learn or not to learn analyzing the role of learning for navigation in virtual environments"
    - "gibson env real world perception for embodied agents"

Connected through hyperedge HE-6 (induced by source paper "efficient bimanual manipulation using learned task schemas"):
  with members:
    - "hierarchical approaches for reinforcement learning in parameterized action space"
    - "active model learning and diverse action sampling for task and motion planning"
    - "learning to guide task and motion planning using score space representation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "meta learning shared hierarchies"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-7 (induced by source paper "accept synthetic objects as real end to end training of attentive deep visuomotor policies for manipulation in clutter"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "craves controlling robotic arm with a vision based economic system"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "deep object centric representations for generalizable robot learning"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "video fill in the blank using lr rl lstms with spatial temporal attentions"

Connected through hyperedge HE-8 (induced by source paper "cyphyhouse a programming simulation and deployment toolchain for heterogeneous distributed coordination"):
  with members:
    - "ros and buzz consensus based behaviors for heterogeneous teams"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "caffe convolutional architecture for fast feature embedding"

Question: Please tell me which class the center node 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)