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: "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
  Abstract: The use of drones for aerial cinematography has revolutionized several applications and industries requiring live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely controlling a drone while filming a moving target usually requires multiple expert human operators; hence the need for an autonomous cinematographer. Current approaches have severe real-life limitations such as requiring scripted scenes that can be solved offline, high-precision motion-capture systems or GPS tags to localize targets, and prior maps of the environment to avoid obstacles and plan for occlusion. #R##N#In this work, we overcome such limitations and propose a complete system for aerial cinematography that combines: (1) a visual pose estimation algorithm for target localization; (2) a real-time incremental 3D signed-distance map algorithm for occlusion and safety computation; and (3) a real-time camera motion planner that optimizes smoothness, collisions, occlusions and artistic guidelines. We evaluate robustness and real-time performance in series of field experiments and simulations by tracking dynamic targets moving through unknown, unstructured environments. Finally, we verify that despite removing previous limitations, our system still matches state-of-the-art performance. Videos of the system in action can be seen at this https URL

Connected through hyperedge HE-1 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "monet unsupervised scene decomposition and representation"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "deep drone racing learning agile flight in dynamic environments"
    - "an algorithmic perspective on imitation learning"
    - "learning latent dynamics for planning from pixels"
    - "world models"
    - "state representation learning for control an overview"

Connected through hyperedge HE-2 (induced by source paper "autonomous aerial cinematography in unstructured environments with learned artistic decision making"):
  with members:
    - "improved generalization of heading direction estimation for aerial filming using semi supervised regression"
    - "can a robot become a movie director learning artistic principles for aerial cinematography"
    - "autonomous drone cinematographer using artistic principles to create smooth safe occlusion free trajectories for aerial filming"
    - "integrating kinematics and environment context into deep inverse reinforcement learning for predicting off road vehicle trajectories"
    - "optimizing for aesthetically pleasing quadrotor camera motion"
    - "good semi supervised learning that requires a bad gan"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-3 (induced by source paper "augmented memory for correlation filters in real time uav tracking"):
  with members:
    - "can a robot become a movie director learning artistic principles for aerial cinematography"
    - "learning aberrance repressed correlation filters for real time uav tracking"
    - "boundary effect aware visual tracking for uav with online enhanced background learning and multi frame consensus verification"
    - "target aware deep tracking"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "kernel cross correlator"
    - "integrating boundary and center correlation filters for visual tracking with aspect ratio variation"

Connected through hyperedge HE-4 (induced by source paper "learning controls using cross modal representations bridging simulation and reality for drone racing"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "monet unsupervised scene decomposition and representation"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "deep drone racing learning agile flight in dynamic environments"
    - "an algorithmic perspective on imitation learning"
    - "learning latent dynamics for planning from pixels"
    - "world models"
    - "state representation learning for control an overview"

Connected through hyperedge HE-5 (induced by source paper "can a robot become a movie director learning artistic principles for aerial cinematography"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "autonomous drone cinematographer using artistic principles to create smooth safe occlusion free trajectories for aerial filming"
    - "optimizing for aesthetically pleasing quadrotor camera motion"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "control of a quadrotor with reinforcement learning"
    - "directing cinematographic drones"
    - "creatism a deep learning photographer capable of creating professional work"

Connected through hyperedge HE-6 (induced by source paper "trajectory optimization for coordinated human robot collaboration"):
  with members:
    - "riemannian motion policies"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "sparse gaussian processes for continuous time trajectory estimation on matrix lie groups"
    - "on the fundamental importance of gauss newton in motion optimization"
    - "newton methods for k order markov constrained motion problems"
    - "continuous inverse optimal control with locally optimal examples"

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)