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: "efficient touch based localization through submodularity"
  Abstract: Many robotic systems deal with uncertainty by performing a sequence of information gathering actions. In this work, we focus on the problem of efficiently constructing such a sequence by drawing an explicit connection to submodularity. Ideally, we would like a method that finds the optimal sequence, taking the minimum amount of time while providing sufficient information. Finding this sequence, however, is generally intractable. As a result, many well-established methods select actions greedily. Surprisingly, this often performs well. Our work first explains this high performance -- we note a commonly used metric, reduction of Shannon entropy, is submodular under certain assumptions, rendering the greedy solution comparable to the optimal plan in the offline setting. However, reacting online to observations can increase performance. Recently developed notions of adaptive submodularity provide guarantees for a greedy algorithm in this online setting. In this work, we develop new methods based on adaptive submodularity for selecting a sequence of information gathering actions online. In addition to providing guarantees, we can capitalize on submodularity to attain additional computational speedups. We demonstrate the effectiveness of these methods in simulation and on a robot.

Connected through hyperedge HE-1 (induced by source paper "in hand object pose tracking via contact feedback and gpu accelerated robotic simulation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "learning to estimate pose and shape of hand held objects from rgb images"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "learning joint reconstruction of hands and manipulated objects"
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "non smooth newton methods for deformable multi body dynamics"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-2 (induced by source paper "learning to grasp without seeing"):
  with members:
    - "semi supervised haptic material recognition for robots using generative adversarial networks"
    - "learning to represent haptic feedback for partially observable tasks"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "cassl curriculum accelerated self supervised learning"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "analysis and observations from the first amazon picking challenge"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-3 (induced by source paper "interactive perception leveraging action in perception and perception in action"):
  with members:
    - "unsupervised learning for physical interaction through video prediction"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "the curious robot learning visual representations via physical interactions"
    - "revisiting active perception"
    - "learning articulated motions from visual demonstration"
    - "learning contact rich manipulation skills with guided policy search"

Connected through hyperedge HE-4 (induced by source paper "active end effector pose selection for tactile object recognition through monte carlo tree search"):
  with members:
    - "interactive perception leveraging action in perception and perception in action"
    - "recovering 6d object pose and predicting next best view in the crowd"
    - "simple regret optimization in online planning for markov decision processes"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (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"
    - "active classification theory and application to underwater inspection"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "analytical slam without linearization"):
  with members:
    - "simultaneous localization and mapping without linearization"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "estimating uncertain spatial relationships in robotics"
    - "from angular manifolds to the integer lattice guaranteed orientation estimation with application to pose graph optimization"

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)