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: "robot assisted feeding generalizing skewering strategies across food items on a realistic plate"
  Abstract: A robot-assisted feeding system must successfully acquire many different food items and transfer them to a user. A key challenge is the wide variation in the physical properties of food, demanding diverse acquisition strategies that are also capable of adapting to previously unseen items. Our key insight is that items with similar physical properties will exhibit similar success rates across an action space, allowing us to generalize to previously unseen items. To better understand which acquisition strategies work best for varied food items, we collected a large, rich dataset of 2450 robot bite acquisition trials for 16 food items with varying properties. Analyzing the dataset provided insights into how the food items' surrounding environment, fork pitch, and fork roll angles affect bite acquisition success. We then developed a bite acquisition framework that takes the image of a full plate as an input, uses RetinaNet to create bounding boxes around food items in the image, and then applies our skewering-position-action network (SPANet) to choose a target food item and a corresponding action so that the bite acquisition success rate is maximized. SPANet also uses the surrounding environment features of food items to predict action success rates. We used this framework to perform multiple experiments on uncluttered and cluttered plates with in-class and out-of-class food items. Results indicate that SPANet can successfully generalize skewering strategies to previously unseen food items.

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:
    - "towards robotic feeding role of haptics in fork based food manipulation"
    - "practical contextual bandits with regression oracles"
    - "focal loss for dense object detection"
    - "bandit based model selection for deformable object manipulation"
    - "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 "learning from failures in robot assisted feeding using online learning to develop manipulation strategies for bite acquisition"):
  with members:
    - "towards robotic feeding role of haptics in fork based food manipulation"
    - "practical contextual bandits with regression oracles"
    - "focal loss for dense object detection"
    - "bandit based model selection for deformable object manipulation"
    - "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"

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)