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: "benchmarking simulated robotic manipulation through a real world dataset"
  Abstract: We present a benchmark to facilitate simulated manipulation; an attempt to overcome the obstacles of physical benchmarks through the distribution of a real world, ground truth dataset. Users are given various simulated manipulation tasks with assigned protocols having the objective of replicating the real world results of a recorded dataset. The benchmark comprises of a range of metrics used to characterise the successes of submitted environments whilst providing insight into their deficiencies. We apply our benchmark to two simulation environments, PyBullet and V-Rep, and publish the results. All materials required to benchmark an environment, including protocols and the dataset, can be found at the benchmarks' website this https URL.

Connected through hyperedge HE-1 (induced by source paper "traversing the reality gap via simulator tuning"):
  with members:
    - "crossing the reality gap with evolved plastic neurocontrollers"
    - "solving rubik s cube with a robot hand"
    - "pyrep bringing v rep to deep robot learning"
    - "learning agile and dynamic motor skills for legged robots"
    - "quantifying the reality gap in robotic manipulation tasks"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real learning agile locomotion for quadruped robots"

Connected through hyperedge HE-2 (induced by source paper "rlbench the robot learning benchmark learning environment"):
  with members:
    - "pyrep bringing v rep to deep robot learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "learning agile and dynamic motor skills for legged robots"
    - "simitate a hybrid imitation learning benchmark"
    - "robonet large scale multi robot learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"
    - "fusion volumetric object level slam"

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)