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: "a grasping centered analysis for cloth manipulation"
  Abstract: Compliant and soft hands have gained a lot of attention in the past decade because of their ability to adapt to the shape of the objects, increasing their effectiveness for grasping. However, when it comes to grasping highly flexible objects such as textiles, we face the dual problem: it is the object that will adapt to the shape of the hand or gripper. In this context, the classic grasp analysis or grasping taxonomies are not suitable for describing textile objects grasps. This work proposes a novel definition of textile object grasps that abstracts from the robotic embodiment or hand shape and recovers concepts from the early neuroscience literature on hand prehension skills. This framework enables us to identify what grasps have been used in literature until now to perform robotic cloth manipulation, and allows for a precise definition of all the tasks that have been tackled in terms of manipulation primitives based on regrasps. In addition, we also review what grippers have been used. Our analysis shows how the vast majority of cloth manipulations have relied only on one type of grasp, and at the same time we identify several tasks that need more variety of grasp types to be executed successfully. Our framework is generic, provides a classification of cloth manipulation primitives and can inspire gripper design and benchmark construction for cloth manipulation.

Connected through hyperedge HE-1 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor"):
  with members:
    - "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"
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"

Connected through hyperedge HE-2 (induced by source paper "deep imitation learning of sequential fabric smoothing policies"):
  with members:
    - "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"
    - "folding deformable objects using predictive simulation and trajectory optimization"
    - "adam a method for stochastic optimization"

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)