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: "folding deformable objects using predictive simulation and trajectory optimization"
  Abstract: Robotic manipulation of deformable objects remains a challenging task. One such task is folding a garment autonomously. Given start and end folding positions, what is an optimal trajectory to move the robotic arm to fold a garment? Certain trajectories will cause the garment to move, creating wrinkles, and gaps, other trajectories will fail altogether. We present a novel solution to find an optimal trajectory that avoids such problematic scenarios. The trajectory is optimized by minimizing a quadratic objective function in an off-line simulator, which includes material properties of the garment and frictional force on the table. The function measures the dissimilarity between a user folded shape and the folded garment in simulation, which is then used as an error measurement to create an optimal trajectory. We demonstrate that our two-arm robot can follow the optimized trajectories, achieving accurate and efficient manipulations of deformable objects.

Connected through hyperedge HE-1 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor"):
  with members:
    - "a grasping centered analysis for cloth manipulation"
    - "model based behavioral cloning with future image similarity learning"
    - "learning to manipulate deformable objects without demonstrations"
    - "dynamic cloth manipulation with deep reinforcement learning"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "deep haptic model predictive control for robot assisted dressing"

Connected through hyperedge HE-2 (induced by source paper "self supervised learning of state estimation for manipulating deformable linear objects"):
  with members:
    - "learning robotic manipulation through visual planning and acting"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "propagation networks for model based control under partial observation"
    - "unsupervised intuitive physics from visual observations"
    - "self supervised visual planning with temporal skip connections"
    - "combining self supervised learning and imitation for vision based rope manipulation"

Connected through hyperedge HE-3 (induced by source paper "realtime simulation of thin shell deformable materials using cnn based mesh embedding"):
  with members:
    - "fast motion planning for high dof robot systems using hierarchical system identification"
    - "hierarchical cloth simulation using deep neural networks"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "tempogan a temporally coherent volumetric gan for super resolution fluid flow"
    - "cloth manipulation using random forest based controller parametrization"
    - "sparse data driven mesh deformation"
    - "variational autoencoders for deforming 3d mesh models"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"

Connected through hyperedge HE-4 (induced by source paper "dynamic cloth manipulation with deep reinforcement learning"):
  with members:
    - "dynamic manipulation of flexible objects with torque sequence using a deep neural network"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "hindsight experience replay"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "learning robust bed making using deep imitation learning with dart"
    - "overcoming exploration in reinforcement learning with demonstrations"

Connected through hyperedge HE-5 (induced by source paper "deep imitation learning of sequential fabric smoothing policies"):
  with members:
    - "a grasping centered analysis for cloth manipulation"
    - "learning dexterous in hand manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "deep haptic model predictive control for robot assisted dressing"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "tracking human pose during robot assisted dressing using single axis capacitive proximity sensing"
    - "cad2rl real single image flight without a single real image"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "feedback based fabric strip folding"):
  with members:
    - "static stability of robotic fabric strip folding"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "learning based feedback controller for deformable object manipulation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "deep reinforcement learning to acquire navigation skills for wheel legged robots in complex environments"
    - "black box data efficient policy search for robotics"

Connected through hyperedge HE-7 (induced by source paper "static stability of robotic fabric strip folding"):
  with members:
    - "learning based feedback controller for deformable object manipulation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "robotic ironing with 3d perception and force torque feedback in household environments"

Connected through hyperedge HE-8 (induced by source paper "minimal work a grasp quality metric for deformable hollow objects"):
  with members:
    - "non planar frictional surface contacts modeling and application to grasping"
    - "deep transfer learning of pick points on fabric for robot bed making"

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)