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: "multi robot transfer learning a dynamical system perspective"
  Abstract: Multi-robot transfer learning allows a robot to use data generated by a second, similar robot to improve its own behavior. The potential advantages are reducing the time of training and the unavoidable risks that exist during the training phase. Transfer learning algorithms aim to find an optimal transfer map between different robots. In this paper, we investigate, through a theoretical study of single-input single-output (SISO) systems, the properties of such optimal transfer maps. We first show that the optimal transfer learning map is, in general, a dynamic system. The main contribution of the paper is to provide an algorithm for determining the properties of this optimal dynamic map including its order and regressors (i.e., the variables it depends on). The proposed algorithm does not require detailed knowledge of the robots' dynamics, but relies on basic system properties easily obtainable through simple experimental tests. We validate the proposed algorithm experimentally through an example of transfer learning between two different quadrotor platforms. Experimental results show that an optimal dynamic map, with correct properties obtained from our proposed algorithm, achieves 60-70% reduction of transfer learning error compared to the cases when the data is directly transferred or transferred using an optimal static map.

Connected through hyperedge HE-1 (induced by source paper "knowledge transfer between robots with similar dynamics for high accuracy impromptu trajectory tracking"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "one shot visual imitation learning via meta learning"
    - "design of deep neural networks as add on blocks for improving impromptu trajectory tracking"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "learning transferable policies for monocular reactive mav control"
    - "distributed iterative learning control for a team of quadrotors"
    - "learning modular neural network policies for multi task and multi robot transfer"

Connected through hyperedge HE-2 (induced by source paper "neural flocking mpc based supervised learning of flocking controllers"):
  with members:
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "declarative vs rule based control for flocking dynamics"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "cleaning tasks knowledge transfer between heterogeneous robots a deep learning approach"):
  with members:
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "deep object centric representations for generalizable robot learning"
    - "vision based multi task manipulation for inexpensive robots using end to end learning from demonstration"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "experience reuse with probabilistic movement primitives"):
  with members:
    - "a survey on deep transfer learning"
    - "sim to real transfer learning using robustified controllers in robotic tasks involving complex dynamics"
    - "building machines that learn and think like people"

Connected through hyperedge HE-5 (induced by source paper "hardware conditioned policies for multi robot transfer learning"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "graph networks as learnable physics engines for inference and control"
    - "hindsight experience replay"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "learning to push by grasping using multiple tasks for effective learning"

Connected through hyperedge HE-6 (induced by source paper "data efficient multirobot multitask transfer learning for trajectory tracking"):
  with members:
    - "adapt zero shot adaptive policy transfer for stochastic dynamical systems"
    - "design of deep neural networks as add on blocks for improving impromptu trajectory tracking"
    - "one shot imitation learning"
    - "learning modular neural network policies for multi task and multi robot transfer"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

Connected through hyperedge HE-7 (induced by source paper "cooperative and distributed reinforcement learning of drones for field coverage"):
  with members:
    - "autonomous uav navigation using reinforcement learning"
    - "a distributed control framework for a team of unmanned aerial vehicles for dynamic wildfire tracking"
    - "safe multi agent reinforcement learning for autonomous driving"

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)