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: "learning to gather information via imitation"
  Abstract: The budgeted information gathering problem - where a robot with a fixed fuel budget is required to maximize the amount of information gathered from the world - appears in practice across a wide range of applications in autonomous exploration and inspection with mobile robots. Although there is an extensive amount of prior work investigating effective approximations of the problem, these methods do not address the fact that their performance is heavily dependent on distribution of objects in the world. In this paper, we attempt to address this issue by proposing a novel data-driven imitation learning framework. #R##N#We present an efficient algorithm, EXPLORE, that trains a policy on the target distribution to imitate a clairvoyant oracle - an oracle that has full information about the world and computes non-myopic solutions to maximize information gathered. We validate the approach on a spectrum of results on a number of 2D and 3D exploration problems that demonstrates the ability of EXPLORE to adapt to different object distributions. Additionally, our analysis provides theoretical insight into the behavior of EXPLORE. Our approach paves the way forward for efficiently applying data-driven methods to the domain of information gathering.

Connected through hyperedge HE-1 (induced by source paper "activemocap optimized drone flight for active human motion capture"):
  with members:
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "yolov3 an incremental improvement"
    - "learn to score efficient 3d scene exploration by predicting view utility"
    - "optimizing for aesthetically pleasing quadrotor camera motion"
    - "human motion capture using a drone"
    - "deep directional statistics pose estimation with uncertainty quantification"
    - "plan3d viewpoint and trajectory optimization for aerial multi view stereo reconstruction"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-2 (induced by source paper "learning q network for active information acquisition"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "real time visual tracking by deep reinforced decision making"
    - "bridging the gap between value and policy based reinforcement learning"
    - "end to end active object tracking via reinforcement learning"
    - "deep reinforcement learning for visual object tracking in videos"
    - "assumed density filtering q learning"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "online multi target tracking using recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "hindsight is only 50 50 unsuitability of mdp based approximate pomdp solvers for multi resolution information gathering"):
  with members:
    - "data driven planning via imitation learning"
    - "learning from the hindsight plan episodic mpc improvement"
    - "pomdp lite for robust robot planning under uncertainty"
    - "configuration lattices for planar contact manipulation under uncertainty"
    - "perception aware path planning"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"
    - "shared autonomy via hindsight optimization"
    - "on the complexity of solving markov decision problems"

Connected through hyperedge HE-4 (induced by source paper "learning heuristic search via imitation"):
  with members:
    - "adaptive information gathering via imitation learning"
    - "learning from the hindsight plan episodic mpc improvement"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
    - "value iteration networks"
    - "prioritized experience replay"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"

Connected through hyperedge HE-5 (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"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "active classification theory and application to underwater inspection"
    - "near optimal bayesian active learning with noisy observations"

Connected through hyperedge HE-6 (induced by source paper "submodular trajectory optimization for aerial 3d scanning"):
  with members:
    - "unrealcv connecting computer vision to unreal engine"
    - "autonomous exploration with a low cost quadrocopter using semi dense monocular slam"
    - "virtual rephotography novel view prediction error for 3d reconstruction"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "fast semidifferential based submodular function optimization"
    - "efficient informative sensing using multiple robots"

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)