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: "ergodic exploration using binary sensing for nonparametric shape estimation"
  Abstract: Current methods to estimate object shape-using either vision or touch-generally depend on high-resolution sensing. Here, we exploit ergodic exploration to demonstrate successful shape estimation when using a low-resolution binary contact sensor. The measurement model is posed as a collision-based tactile measurement, and classification methods are used to discriminate between shape boundary regions in the search space. Posterior likelihood estimates of the measurement model help the system actively seek out regions where the binary sensor is most likely to return informative measurements. Results show successful shape estimation of various objects as well as the ability to identify multiple objects in an environment. Interestingly, it is shown that ergodic exploration utilizes noncontact motion to gather significant information about shape. The algorithm is extended in three dimensions in simulation, and we present two-dimensional experimental results using the Rethink Baxter robot.

Connected through hyperedge HE-1 (induced by source paper "decentralized ergodic control distribution driven sensing and exploration for multiagent systems"):
  with members:
    - "distributed active state estimation with user specified accuracy"
    - "real time area coverage and target localization using receding horizon ergodic exploration"
    - "sequential action control closed form optimal control for nonlinear and nonsmooth systems"
    - "ergodic exploration of distributed information"

Connected through hyperedge HE-2 (induced by source paper "data driven measurement models for active localization in sparse environments"):
  with members:
    - "real time area coverage and target localization using receding horizon ergodic exploration"
    - "synthesizing training data for object detection in indoor scenes"
    - "ergodic exploration of distributed information"

Connected through hyperedge HE-3 (induced by source paper "robotic tactile perception of object properties a review"):
  with members:
    - "connecting look and feel associating the visual and tactile properties of physical materials"
    - "knock knock acoustic object recognition by using stacked denoising autoencoders"
    - "memory unscented particle filter for 6 dof tactile localization"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"
    - "iterative closest labeled point for tactile object shape recognition"
    - "localizing the object contact through matching tactile features with visual map"
    - "deep learning for tactile understanding from visual and haptic data"
    - "representation learning a review and new perspectives"

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)