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: "active classification theory and application to underwater inspection"
  Abstract: We discuss the problem in which an autonomous vehicle must classify an object based on multiple views. We focus on the active classification setting, where the vehicle controls which views to select to best perform the classification. The problem is formulated as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We formally analyze the benefit of acting adaptively as new information becomes available. The analysis leads to a probabilistic algorithm for determining the best views to observe based on information theoretic costs. We validate our approach in two ways, both related to underwater inspection: 3D polyhedra recognition in synthetic depth maps and ship hull inspection with imaging sonar. These tasks encompass both the planning and recognition aspects of the active classification problem. The results demonstrate that actively planning for informative views can reduce the number of necessary views by up to 80% when compared to passive methods.

Connected through hyperedge HE-1 (induced by source paper "cost bounded active classification using partially observable markov decision processes"):
  with members:
    - "barrier certificates for assured machine teaching"
    - "bounded policy synthesis for pomdps with safe reachability objectives"
    - "privacy verification in pomdps via barrier certificates"
    - "pomdp model learning for human robot collaboration"
    - "synthesis of surveillance strategies via belief abstraction"

Connected through hyperedge HE-2 (induced by source paper "data driven planning via imitation learning"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "deep reinforcement learning a brief survey"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "learning sampling distributions for robot motion planning"
    - "near optimal edge evaluation in explicit generalized binomial graphs"
    - "qmdp net deep learning for planning under partial observability"
    - "learning from the hindsight plan episodic mpc improvement"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-3 (induced by source paper "adaptive information gathering via imitation learning"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "pomdp lite for robust robot planning under uncertainty"
    - "near optimal bayesian active learning with correlated and noisy tests"
    - "plato policy learning using adaptive trajectory optimization"
    - "learning to gather information via imitation"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "near optimal bayesian active learning with noisy observations"

Connected through hyperedge HE-4 (induced by source paper "learning to gather information via imitation"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "near optimal bayesian active learning with correlated and noisy tests"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "efficient touch based localization through submodularity"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "learning and optimization with submodular functions"):
  with members:
    - "active sequential hypothesis testing"
    - "submodular inference of diffusion networks from multiple trees"
    - "near optimal nonmyopic value of information in graphical models"
    - "uncovering the temporal dynamics of diffusion networks"
    - "learning with submodular functions a convex optimization perspective"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-6 (induced by source paper "active sequential hypothesis testing"):
  with members:
    - "controlled sensing for multihypothesis testing"
    - "errors and erasures decoding for block codes with feedback"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"
    - "note on noisy group testing asymptotic bounds and belief propagation reconstruction"
    - "randomized sensor selection in sequential hypothesis testing"
    - "a simple converse of burnashev s reliability function"
    - "error exponents for variable length block codes with feedback and cost constraints"

Connected through hyperedge HE-7 (induced by source paper "sequentiality and adaptivity gains in active hypothesis testing"):
  with members:
    - "active sequential hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "universal and composite hypothesis testing via mismatched divergence"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"
    - "robust hypothesis testing with a relative entropy tolerance"

Connected through hyperedge HE-8 (induced by source paper "efficient touch based localization through submodularity"):
  with members:
    - "point based pomdp algorithms improved analysis and implementation"
    - "efficient test selection in active diagnosis via entropy approximation"
    - "near optimal nonmyopic value of information in graphical models"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "online planning algorithms for pomdps"
    - "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)