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: "quasi direct drive for low cost compliant robotic manipulation"
  Abstract: Robots must cost less and be force-controlled to enable widespread, safe deployment in unconstrained human environments. We propose Quasi-Direct Drive actuation as a capable paradigm for robotic force-controlled manipulation in human environments at low-cost. Our prototype - Blue - is a human scale 7 Degree of Freedom arm with 2kg payload. Blue can cost less than $5000. We show that Blue has dynamic properties that meet or exceed the needs of human operators: the robot has a nominal position-control bandwidth of 7.5Hz and repeatability within 4mm. We demonstrate a Virtual Reality based interface that can be used as a method for telepresence and collecting robot training demonstrations. Manufacturability, scaling, and potential use-cases for the Blue system are also addressed. Videos and additional information can be found online at this http URL

Connected through hyperedge HE-1 (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"
    - "challenges of real world reinforcement learning"
    - "pyrobot an open source robotics framework for research and benchmarking"

Connected through hyperedge HE-2 (induced by source paper "deepracer educational autonomous racing platform for experimentation with sim2real reinforcement learning"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "f1 10 an open source autonomous cyber physical platform"
    - "mushr a low cost open source robotic racecar for education and research"
    - "self driving scale car trained by deep reinforcement learning"
    - "an online learning approach to model predictive control"
    - "learning agile and dynamic motor skills for legged robots"
    - "demonstration guided deep reinforcement learning of control policies for dexterous human robot interaction"
    - "deepmind control suite"

Connected through hyperedge HE-3 (induced by source paper "doorgym a scalable door opening environment and baseline agent"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "roboturk a crowdsourcing platform for robotic skill learning through imitation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-4 (induced by source paper "a mobile manipulation system for one shot teaching of complex tasks in homes"):
  with members:
    - "the double sphere camera model"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "visual inertial teach and repeat for aerial inspection"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "the devil is in the tails fine grained classification in the wild"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-5 (induced by source paper "an open torque controlled modular robot architecture for legged locomotion research"):
  with members:
    - "stanford doggo an open source quasi direct drive quadruped"
    - "learning a structured neural network policy for a hopping task"
    - "sim to real learning agile locomotion for quadruped robots"
    - "oncilla robot a versatile open source quadruped research robot with compliant pantograph legs"
    - "cable driven actuation for highly dynamic robotic systems"
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"
    - "structured contact force optimization for kino dynamic motion generation"

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)