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 "adaptive robot assisted feeding an online learning framework for acquiring previously unseen food items"):
  Abstract: A successful robot-assisted feeding system requires bite acquisition of a wide variety of food items. It needs to adapt to changing user food preferences under uncertain visual and physical environments. Different food items in different environmental conditions may require different manipulation strategies for successful bite acquisition. Therefore, a key challenge is to handle previously-unseen food items with very different success rate distributions over strategy. Combining low-level controllers and planners into discrete action trajectories, we show that the problem can be represented using a linear contextual bandit setting. We construct a simulated environment using a doubly-robust loss estimate from previously-seen food items, which we use to tune the parameters of off-the-shelf contextual bandit algorithms. Finally, we demonstrate empirically on a robot-assisted feeding system that, even starting with a model trained on thousands of skewering attempts on dissimilar previously-seen food items, $\epsilon$-greedy and LinUCB algorithms can quickly converge to the most successful manipulation strategy.

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 "learning from failures in robot assisted feeding using online learning to develop manipulation strategies for bite acquisition")
  Member "towards robotic feeding role of haptics in fork based food manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robot assisted feeding generalizing skewering strategies across food items on a realistic plate")
  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 "accelerated learning from recommender systems using multi armed bandit")
  Member "towards assistive feeding with a general purpose mobile manipulator":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robot assisted feeding generalizing skewering strategies across food items on a realistic plate")
  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 "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation")

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)