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: "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"
  Abstract: We introduce Air Learning, an AI research platform for benchmarking algorithm-hardware performance and energy efficiency trade-offs. We focus in particular on deep reinforcement learning (RL) interactions in autonomous unmanned aerial vehicles (UAVs). Equipped with a random environment generator, AirLearning exposes a UAV to a diverse set of challenging scenarios. Users can specify a task, train different RL policies and evaluate their performance and energy efficiency on a variety of hardware platforms. To show how Air Learning can be used, we seed it with Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) to solve a point-to-point obstacle avoidance task in three different environments, generated using our configurable environment generator. We train the two algorithms using curriculum learning and non-curriculum-learning. Air Learning assesses the trained policies' performance, under a variety of quality-of-flight (QoF) metrics, such as the energy consumed, endurance and the average trajectory length, on resource-constrained embedded platforms like a Ras-Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to 79.43% longer trajectories in one of the environments. To understand the source of such differences, we use Air Learning to artificially degrade desktop performance to mimic what happens on a low-end embedded system. QoF metrics with hardware-in-the-loop characterize those differences and expose how the choice of onboard compute affects the aerial robot's performance. We also conduct reliability studies to demonstrate how Air Learning can help understand how sensor failures affect the learned policies. All put together, Air Learning enables a broad class of RL studies on UAVs. More information and code for Air Learning can be found here: this http URL.

Connected through hyperedge HE-1 (induced by source paper "learning to seek deep reinforcement learning for phototaxis of a nano drone in an obstacle field"):
  with members:
    - "low level control of a quadrotor with deep model based reinforcement learning"
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "fly safe aerial swarm robotics using force field particle swarm optimisation"
    - "evolving rewards to automate reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning for uav attitude control"

Connected through hyperedge HE-2 (induced by source paper "quantized reinforcement learning quarl"):
  with members:
    - "defensive quantization when efficiency meets robustness"
    - "model compression via distillation and quantization"
    - "per tensor fixed point quantization of the back propagation algorithm"
    - "sbnet sparse blocks network for fast inference"
    - "pruned and structurally sparse neural networks"
    - "relaxed quantization for discretized neural networks"
    - "learning to drive in a day"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-3 (induced by source paper "online deep reinforcement learning for autonomous uav navigation and exploration of outdoor environments"):
  with members:
    - "visual appearance analysis of forest scenes for monocular slam"
    - "multi task regression based learning for autonomous unmanned aerial vehicle flight control within unstructured outdoor environments"
    - "transfer and online reinforcement learning in stt mram based embedded systems for autonomous drones"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "rainbow combining improvements in deep reinforcement learning"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "learning what data to learn"

Connected through hyperedge HE-4 (induced by source paper "flight controller synthesis via deep reinforcement learning"):
  with members:
    - "neuroflight next generation flight control firmware"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "learning dexterous in hand manipulation"
    - "reinforcement learning for uav attitude control"
    - "end to end analysis and design of a drone flight controller"
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "deep reinforcement learning that matters"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-5 (induced by source paper "learning to seek autonomous source seeking on a nano drone microcontroller with deep reinforcement learning"):
  with members:
    - "low level control of a quadrotor with deep model based reinforcement learning"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "evolving rewards to automate reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning for uav attitude control"
    - "soft actor critic algorithms and applications"
    - "ultra low power deep learning powered autonomous nano drones"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

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)