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: "exploration in interactive personalized music recommendation a reinforcement learning approach"
  Abstract: Current music recommender systems typically act in a greedy fashion by recommending songs with the highest user ratings. Greedy recommendation, however, is suboptimal over the long term: it does not actively gather information on user preferences and fails to recommend novel songs that are potentially interesting. A successful recommender system must balance the needs to explore user preferences and to exploit this information for recommendation. This paper presents a new approach to music recommendation by formulating this exploration-exploitation trade-off as a reinforcement learning task called the multi-armed bandit. To learn user preferences, it uses a Bayesian model, which accounts for both audio content and the novelty of recommendations. A piecewise-linear approximation to the model and a variational inference algorithm are employed to speed up Bayesian inference. One additional benefit of our approach is a single unified model for both music recommendation and playlist generation. Both simulation results and a user study indicate strong potential for the new approach.

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"
    - "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"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-2 (induced by source paper "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation"):
  with members:
    - "signed distance based deep memory recommender"
    - "a hybrid approach to music playlist continuation based on playlist song membership"
    - "regularizing matrix factorization with user and item embeddings for recommendation"
    - "collaborative memory network for recommendation systems"
    - "gated attentive autoencoder for content aware recommendation"
    - "adversarial personalized ranking for recommendation"
    - "point of interest recommendation exploiting self attentive autoencoders with neighbor aware influence"
    - "recurrent neural networks with top k gains for session based recommendations"

Connected through hyperedge HE-3 (induced by source paper "eyecar modeling the visual attention allocation of drivers in semi autonomous vehicles"):
  with members:
    - "a deep reinforcement learning based driving policy for autonomous road vehicles"
    - "driving with style inverse reinforcement learning in general purpose planning for automated driving"
    - "agil learning attention from human for visuomotor tasks"
    - "the effect of whole body haptic feedback on driver s perception in negotiating a curve"
    - "joint attention in driver pedestrian interaction from theory to practice"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "predicting driver attention in critical situations"
    - "predicting the driver s focus of attention the dr eye ve project"

Connected through hyperedge HE-4 (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"
    - "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"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-5 (induced by source paper "beyond personalization social content recommendation for creator equality and consumer satisfaction"):
  with members:
    - "session based social recommendation via dynamic graph attention networks"
    - "large scale interactive recommendation with tree structured policy gradient"
    - "neural collaborative filtering"
    - "collaborative filtering with social local models"
    - "collaborative deep learning for recommender systems"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "context tree for adaptive session based recommendation"):
  with members:
    - "evaluation of session based recommendation algorithms"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "recurrent latent variable networks for session based recommendation"
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "session based recommendations with recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-7 (induced by source paper "explore exploit a framework for interactive and online learning"):
  with members:
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "neural architecture search with reinforcement learning"
    - "online sequence to sequence active learning for open domain dialogue generation"
    - "algorithms for multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-8 (induced by source paper "sherlock sparse hierarchical embeddings for visually aware one class collaborative filtering"):
  with members:
    - "fashionista a fashion aware graphical system for exploring visually similar items"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "image based recommendations on styles and substitutes"
    - "caffe convolutional architecture for fast feature embedding"
    - "bpr bayesian personalized ranking from implicit feedback"

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.MM(Multimedia)