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 end effector pose selection for tactile object recognition through monte carlo tree search"
  Abstract: This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.

Connected through hyperedge HE-1 (induced by source paper "action for better prediction"):
  with members:
    - "self supervised exploration via disagreement"
    - "curious ilqr resolving uncertainty in model based rl"
    - "model based reinforcement learning for atari"
    - "manipulation by feel touch based control with deep predictive models"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "robonet large scale multi robot learning"
    - "learning predictive models from observation and interaction"
    - "exploration by random network distillation"

Connected through hyperedge HE-2 (induced by source paper "triangle net towards robustness in point cloud classification"):
  with members:
    - "rotation invariant convolutions for 3d point clouds deep learning"
    - "a deeper look at 3d shape classifiers"
    - "learning 3d shape completion under weak supervision"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-3 (induced by source paper "safer deep rl with shallow mcts a case study in pommerman"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "real time tree search with pessimistic scenarios"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "wisemove a framework for safe deep reinforcement learning for autonomous driving"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "monte carlo tree search for simulation based strategy analysis"
    - "backplay man muss immer umkehren"
    - "deep multi agent reinforcement learning with relevance graphs"

Connected through hyperedge HE-4 (induced by source paper "tactile mapping and localization from high resolution tactile imprints"):
  with members:
    - "learning dexterous in hand manipulation"
    - "3d shape perception from monocular vision touch and shape priors"
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "iclap shape recognition by combining proprioception and touch sensing"
    - "dense tactile force distribution estimation using gelslim and inverse fem"
    - "tactile regrasp grasp adjustments via simulated tactile transformations"
    - "robotic tactile perception of object properties a review"
    - "shape independent hardness estimation using deep learning and a gelsight tactile sensor"

Connected through hyperedge HE-5 (induced by source paper "using monte carlo tree search as a demonstrator within asynchronous deep rl"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "backplay man muss immer umkehren"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "surprising negative results for generative adversarial tree search"
    - "learning montezuma s revenge from a single demonstration"
    - "pommerman a multi agent playground"
    - "deep reinforcement learning a brief survey"

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)