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 "multi modal active perception for information gathering in science missions"):
  Abstract: Robotic science missions in remote environments, such as deep ocean and outer space, can involve studying phenomena that cannot directly be observed using on-board sensors but must be deduced by combining measurements of correlated variables with domain knowledge. Traditionally, in such missions, robots passively gather data along prescribed paths, while inference, path planning, and other high level decision making is largely performed by a supervisory science team. However, communication constraints hinder these processes, and hence the rate of scientific progress. This paper presents an active perception approach that aims to reduce robots' reliance on human supervision and improve science productivity by encoding scientists' domain knowledge and decision making process on-board. We use Bayesian networks to compactly model critical aspects of scientific knowledge while remaining robust to observation and modeling uncertainty. We then formulate path planning and sensor scheduling as an information gain maximization problem, and propose a sampling-based solution based on Monte Carlo tree search to plan informative sensing actions which exploit the knowledge encoded in the network. The computational complexity of our framework does not grow with the number of observations taken and allows long horizon planning in an anytime manner, making it highly applicable to field robotics. Simulation results show statistically significant performance improvements over baseline methods, and we validate the practicality of our approach through both hardware experiments and simulated experiments with field data gathered during the NASA Mojave Volatiles Prospector science expedition.

Includes members:
  Member "data driven learning and planning for environmental sampling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "state continuity approximation of markov decision processes via finite element analysis for autonomous system planning")
  Member "an approach to autonomous science by modeling geological knowledge in a bayesian framework":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "active reward learning for co robotic vision based exploration in bandwidth limited environments")
  Member "modeling curiosity in a mobile robot for long term autonomous exploration and monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "active reward learning for co robotic vision based exploration in bandwidth limited environments")
  Member "loopy belief propagation for approximate inference an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "learning bayesian networks the combination of knowledge and statistical data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "finding approximate pomdp solutions through belief compression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a hamilton jacobi reachability based framework for predicting and analyzing human motion for safe planning")

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)