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 compliant underactuated hand for robust manipulation"
  Abstract: This paper introduces the i-HY Hand, an underactuated hand driven by 5 actuators that is capable of performing a wide range of grasping and in-hand manipulation tasks. This hand was designed to address the need for a durable, inexpensive, moderately dexterous hand suitable for use on mobile robots. The primary focus of this paper will be on the novel minimalistic design of i-HY, which was developed by choosing a set of target tasks around which the design of the hand was optimized. Particular emphasis is placed on the development of underactuated fingers that are capable of both firm power grasps and low- stiffness fingertip grasps using only the passive mechanics of the finger mechanism. Experimental results demonstrate successful grasping of a wide range of target objects, the stability of fingertip grasping, as well as the ability to adjust the force exerted on grasped objects using the passive finger mechanics.

Connected through hyperedge HE-1 (induced by source paper "contact less manipulation of millimeter scale objects via ultrasonic levitation"):
  with members:
    - "improved proximity contact and force sensing via optimization of elastomer air interface geometry"
    - "a framework for robot manipulation skill formalism meta learning and adaptive control"
    - "learning dexterous manipulation for a soft robotic hand from human demonstration"

Connected through hyperedge HE-2 (induced by source paper "tactile model o fabrication and testing of a 3d printed three fingered tactile robot hand"):
  with members:
    - "learning to identify object instances by touch tactile recognition via multimodal matching"
    - "gelslim a high resolution compact robust and calibrated tactile sensing finger"
    - "in hand object stabilization by independent finger control"
    - "from pixels to percepts highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "improved gelsight tactile sensor for measuring geometry and slip"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "active learning of probabilistic movement primitives"):
  with members:
    - "adaptation and robust learning of probabilistic movement primitives"
    - "relaxed rigidity constraints kinematic trajectory optimization and collision avoidance for in grasp manipulation"
    - "probabilistic object tracking using a range camera"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "active imitation learning via reduction to i i d active learning"
    - "interactive policy learning through confidence based autonomy"

Connected through hyperedge HE-4 (induced by source paper "on policy learning robust to irreversible events an application to robotic in hand manipulation"):
  with members:
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "learning dexterous manipulation for a soft robotic hand from human demonstration"
    - "learning dexterous manipulation policies from experience and imitation"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-5 (induced by source paper "a double jaw hand designed for multi object assembly"):
  with members:
    - "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"
    - "teaching robots to do object assembly using multi modal 3d vision"

Connected through hyperedge HE-6 (induced by source paper "a hand combining two simple grippers to pick up and arrange objects for assembly"):
  with members:
    - "analyzing the utility of a support pin in sequential robotic manipulation"

Connected through hyperedge HE-7 (induced by source paper "steering a particle swarm using global inputs and swarm statistics"):
  with members:
    - "crowdsourcing swarm manipulation experiments a massive online user study with large swarms of simple robots"
    - "on the expected complexity of random convex hulls"

Connected through hyperedge HE-8 (induced by source paper "on neuromechanical approaches for the study of biological grasp and manipulation"):
  with members:
    - "probabilistic articulated real time tracking for robot manipulation"

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)