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: "an approach to autonomous science by modeling geological knowledge in a bayesian framework"
  Abstract: Autonomous Science is a field of study which aims to extend the autonomy of exploration robots from low level functionality, such as on-board perception and obstacle avoidance, to science autonomy, which allows scientists to specify missions at task level. This will enable more remote and extreme environments such as deep ocean and other planets to be studied, leading to significant science discoveries. This paper presents an approach to extend the high level autonomy of robots by enabling them to model and reason about scientific knowledge on-board. We achieve this by using Bayesian networks to encode scientific knowledge and adapting Monte Carlo Tree Search techniques to reason about the network and plan informative sensing actions. The resulting knowledge representation and reasoning framework is anytime, handles large state spaces and robust to uncertainty making it highly applicable to field robotics. We apply the approach to a Mars exploration mission in which the robot is required to plan paths and decide when to use its sensing modalities to study a scientific latent variable of interest. Extensive simulation results show that our approach has significant performance benefits over alternative methods. We also demonstrate the practicality of our approach in an analog Martian environment where our experimental rover, Continuum, plans and executes a science mission autonomously.

Connected through hyperedge HE-1 (induced by source paper "active reward learning for co robotic vision based exploration in bandwidth limited environments"):
  with members:
    - "information guided robotic maximum seek and sample in partially observable continuous environments"
    - "streaming scene maps for co robotic exploration in bandwidth limited environments"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "feature discovery and visualization of robot mission data using convolutional autoencoders and bayesian nonparametric topic models"
    - "modeling curiosity in a mobile robot for long term autonomous exploration and monitoring"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "anomaly detection in unstructured environments using bayesian nonparametric scene modeling"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-2 (induced by source paper "a versatile multi robot monte carlo tree search planner for on line coverage path planning"):
  with members:
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "efficient multi robot coverage of a known environment"
    - "discrete partitioning and coverage control for gossiping robots"

Connected through hyperedge HE-3 (induced by source paper "streaming scene maps for co robotic exploration in bandwidth limited environments"):
  with members:
    - "modeling curiosity in a mobile robot for long term autonomous exploration and monitoring"
    - "gibbs sampling strategies for semantic perception of streaming video data"
    - "anomaly detection in unstructured environments using bayesian nonparametric scene modeling"

Connected through hyperedge HE-4 (induced by source paper "multi modal active perception for information gathering in science missions"):
  with members:
    - "data driven learning and planning for environmental sampling"
    - "modeling curiosity in a mobile robot for long term autonomous exploration and monitoring"
    - "loopy belief propagation for approximate inference an empirical study"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "finding approximate pomdp solutions through belief compression"

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)