human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning from failures in robot assisted feeding using online learning to develop manipulation strategies for bite acquisition"):
  Abstract: Successful robot-assisted feeding requires bite acquisition of a wide variety of food items. Different food items may require different manipulation actions for successful bite acquisition. Therefore, a key challenge is to handle previously-unseen food items with very different action distributions. By leveraging contexts from previous bite acquisition attempts, a robot should be able to learn online how to acquire those previously-unseen food items. In this ongoing work, we construct a contextual bandit framework for this problem setting. We then propose variants of the $\epsilon$-greedy and LinUCB contextual bandit algorithms to minimize cumulative regret within that setting. In future, we expect empirical estimates of cumulative regret for each algorithm on robot bite acquisition trials as well as updated theoretical regret bounds that leverage the more structured context of this problem setting.

Includes members:
  Member "robot assisted feeding generalizing skewering strategies across food items on a realistic plate":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive robot assisted feeding an online learning framework for acquiring previously unseen food items")
  Member "towards robotic feeding role of haptics in fork based food manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive robot assisted feeding an online learning framework for acquiring previously unseen food items")
  Member "practical contextual bandits with regression oracles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "bandit based model selection for deformable object manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive robot assisted feeding an online learning framework for acquiring previously unseen food items")
  Member "towards assistive feeding with a general purpose mobile manipulator":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive robot assisted feeding an online learning framework for acquiring previously unseen food items")
  Member "taming the monster a fast and simple algorithm for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "exploration in interactive personalized music recommendation a reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adaptive robot assisted feeding an online learning framework for acquiring previously unseen food items")

Question: Please tell me which class the source paper 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)