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: "bandit based model selection for deformable object manipulation"
  Abstract: We present a novel approach to deformable object manipulation that does not rely on highly-accurate modeling. The key contribution of this paper is to formulate the task as a Multi-Armed Bandit problem, with each arm representing a model of the deformable object. To "pull" an arm and evaluate its utility, we use the arm's model to generate a velocity command for the gripper(s) holding the object and execute it. As the task proceeds and the object deforms, the utility of each model can change. Our framework estimates these changes and balances exploration of the model set with exploitation of high-utility models. We also propose an approach based on Kalman Filtering for Non-stationary Multi-armed Normal Bandits (KF-MANB) to leverage the coupling between models to learn more from each arm pull. We demonstrate that our method outperforms previous methods on synthetic trials, and performs competitively on several manipulation tasks in simulation.

Connected through hyperedge HE-1 (induced by source paper "adaptive robot assisted feeding an online learning framework for acquiring previously unseen food items"):
  with members:
    - "robot assisted feeding generalizing skewering strategies across food items on a realistic plate"
    - "towards robotic feeding role of haptics in fork based food manipulation"
    - "practical contextual bandits with regression oracles"
    - "focal loss for dense object detection"
    - "towards assistive feeding with a general purpose mobile manipulator"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "exploration in interactive personalized music recommendation a reinforcement learning approach"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-2 (induced by source paper "accelerated learning from recommender systems using multi armed bandit"):
  with members:
    - "session based recommendation with graph neural network"
    - "productization challenges of contextual multi armed bandits"
    - "perturbed history exploration in stochastic multi armed bandits"
    - "on the difficulty of evaluating baselines a study on recommender systems"
    - "a batched multi armed bandit approach to news headline testing"
    - "a simple deep personalized recommendation system"
    - "a survey on session based recommender systems"
    - "multiplayer bandits without observing collision information"

Connected through hyperedge HE-3 (induced by source paper "learning from failures in robot assisted feeding using online learning to develop manipulation strategies for bite acquisition"):
  with members:
    - "robot assisted feeding generalizing skewering strategies across food items on a realistic plate"
    - "towards robotic feeding role of haptics in fork based food manipulation"
    - "practical contextual bandits with regression oracles"
    - "focal loss for dense object detection"
    - "towards assistive feeding with a general purpose mobile manipulator"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "exploration in interactive personalized music recommendation a reinforcement learning approach"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "manipulating highly deformable materials using a visual feedback dictionary"):
  with members:
    - "learning to navigate cloth using haptics"

Connected through hyperedge HE-5 (induced by source paper "3d deformable object manipulation using fast online gaussian process regression"):
  with members:
    - "folding deformable objects using predictive simulation and trajectory 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)