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 "adaptive information gathering via imitation learning"):
  Abstract: In the adaptive information gathering problem, a policy is required to select an informative sensing location using the history of measurements acquired thus far. While there is an extensive amount of prior work investigating effective practical approximations using variants of Shannon's entropy, the efficacy of such policies heavily depends on the geometric distribution of objects in the world. On the other hand, the principled approach of employing online POMDP solvers is rendered impractical by the need to explicitly sample online from a posterior distribution of world maps. #R##N#We present a novel data-driven imitation learning framework to efficiently train information gathering policies. The policy imitates a clairvoyant oracle - an oracle that at train time has full knowledge about the world map and can compute maximally informative sensing locations. We analyze the learnt policy by showing that offline imitation of a clairvoyant oracle is implicitly equivalent to online oracle execution in conjunction with posterior sampling. This observation allows us to obtain powerful near-optimality guarantees for information gathering problems possessing an adaptive sub-modularity property. As demonstrated on a spectrum of 2D and 3D exploration problems, the trained policies enjoy the best of both worlds - they adapt to different world map distributions while being computationally inexpensive to evaluate.

Includes members:
  Member "approximation algorithms for optimal decision trees and adaptive tsp problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a tight analysis of greedy yields subexponential time approximation for uniform decision tree")
  Member "pomdp lite for robust robot planning under uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges")
  Member "near optimal bayesian active learning with correlated and noisy tests":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning in confusion batch active learning with noisy oracle")
  Member "plato policy learning using adaptive trajectory optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adapt zero shot adaptive policy transfer for stochastic dynamical systems")
  Member "learning to gather information via imitation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "activemocap optimized drone flight for active human motion capture")
  Member "reinforcement and imitation learning via interactive no regret learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning hierarchical teaching policies for cooperative agents")
  Member "submodular optimization with submodular cover and submodular knapsack constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust submodular minimization with applications to cooperative modeling")
  Member "active classification theory and application to underwater inspection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cost bounded active classification using partially observable markov decision processes")

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)