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: "replab a reproducible low cost arm benchmark platform for robotic learning"
  Abstract: Standardized evaluation measures have aided in the progress of machine learning approaches in disciplines such as computer vision and machine translation. In this paper, we make the case that robotic learning would also benefit from benchmarking, and present the "REPLAB" platform for benchmarking vision-based manipulation tasks. REPLAB is a reproducible and self-contained hardware stack (robot arm, camera, and workspace) that costs about 2000 USD, occupies a cuboid of size 70x40x60 cm, and permits full assembly within a few hours. Through this low-cost, compact design, REPLAB aims to drive wide participation by lowering the barrier to entry into robotics and to enable easy scaling to many robots. We envision REPLAB as a framework for reproducible research across manipulation tasks, and as a step in this direction, we define a template for a grasping benchmark consisting of a task definition, evaluation protocol, performance measures, and a dataset of 92k grasp attempts. We implement, evaluate, and analyze several previously proposed grasping approaches to establish baselines for this benchmark. Finally, we also implement and evaluate a deep reinforcement learning approach for 3D reaching tasks on our REPLAB platform. Project page with assembly instructions, code, and videos: this https URL.

Connected through hyperedge HE-1 (induced by source paper "rigid soft interactive learning for robust grasping"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "analysis and observations from the first amazon picking challenge"
    - "the curious robot learning visual representations via physical interactions"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "real time grasp detection using convolutional neural networks"

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:
    - "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"
    - "pyrobot an open source robotics framework for research and benchmarking"

Connected through hyperedge HE-3 (induced by source paper "pyrobot an open source robotics framework for research and benchmarking"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "habitat a platform for embodied ai research"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "gibson env real world perception for embodied agents"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-4 (induced by source paper "robel robotics benchmarks for learning with low cost robots"):
  with members:
    - "active domain randomization"
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "sim to real reinforcement learning for deformable object manipulation"

Connected through hyperedge HE-5 (induced by source paper "morphology agnostic visual robotic control"):
  with members:
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "contingency aware exploration in reinforcement learning"
    - "towards a generic diver following algorithm balancing robustness and efficiency in deep visual detection"
    - "learning visual servoing with deep features and fitted q iteration"
    - "self supervised visual planning with temporal skip connections"
    - "underwater multi robot convoying using visual tracking by detection"
    - "learning a visuomotor controller for real world robotic grasping using easily simulated depth images"
    - "sim2real view invariant visual servoing by recurrent control"

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)