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: "feature based transfer learning for robotic push manipulation"
  Abstract: This paper presents a data-efficient approach to learning transferable forward models for robotic push manipulation. Our approach extends our previous work on contact-based predictors by leveraging information on the pushed object's local surface features. We test the hypothesis that, by conditioning predictions on local surface features, we can achieve generalisation across objects of different shapes. In doing so, we do not require a CAD model of the object but rather rely on a point cloud object model (PCOM). Our approach involves learning motion models that are specific to contact models. Contact models encode the contacts seen during training time and allow generating similar contacts at prediction time. Predicting on familiar ground reduces the motion models' sample complexity while using local contact information for prediction increases their transferability. In extensive experiments in simulation, our approach is capable of transfer learning for various test objects, outperforming a baseline predictor. We support those results with a proof of concept on a real robot.

Connected through hyperedge HE-1 (induced by source paper "let s push things forward a survey on robot pushing"):
  with members:
    - "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video"
    - "a data efficient approach to precise and controlled pushing"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "a fast stochastic contact model for planar pushing and grasping theory and experimental validation"
    - "deep predictive policy training using reinforcement learning"
    - "a probabilistic data driven model for planar pushing"
    - "information theoretic model identification and policy search using physics engines with application to robotic manipulation"

Connected through hyperedge HE-2 (induced by source paper "grasping and manipulation with a multi fingered hand"):
  with members:
    - "let s push things forward a survey on robot pushing"
    - "multisensory learning framework for robot drumming"
    - "robust and fast generation of top and side grasps for unknown objects"
    - "metrics and benchmarks for remote shared controllers in industrial applications"
    - "automatic detection of myocontrol failures based upon situational context information"
    - "hypothesis based belief planning for dexterous grasping"
    - "online planning algorithms for pomdps"
    - "perseus randomized point based value iteration for pomdps"

Connected through hyperedge HE-3 (induced by source paper "aging touch systematic and unbiased presentation of tactile stimuli"):
  with members:
    - "grasping and manipulation with a multi fingered hand"
    - "2d linear time variant controller for human s intention detection for reach to grasp trajectories in novel scenes"
    - "metrics and benchmarks for remote shared controllers in industrial applications"
    - "automatic detection of myocontrol failures based upon situational context information"
    - "hypothesis based belief planning for dexterous grasping"

Connected through hyperedge HE-4 (induced by source paper "automatic detection of myocontrol failures based upon situational context information"):
  with members:
    - "hypothesis based belief planning for dexterous grasping"

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)