human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "autonomous aerial cinematography in unstructured environments with learned artistic decision making"):
  Abstract: Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple expert human operators. Hence, there is demand for an autonomous cinematographer that can reason about both geometry and scene context in real-time. Existing approaches do not address all aspects of this problem; they either require high-precision motion-capture systems or GPS tags to localize targets, rely on prior maps of the environment, plan for short time horizons, or only follow artistic guidelines specified before flight. #R##N#In this work, we address the problem in its entirety and propose a complete system for real-time aerial cinematography that for the first time combines: (1) vision-based target estimation; (2) 3D signed-distance mapping for occlusion estimation; (3) efficient trajectory optimization for long time-horizon camera motion; and (4) learning-based artistic shot selection. We extensively evaluate our system both in simulation and in field experiments by filming dynamic targets moving through unstructured environments. Our results indicate that our system can operate reliably in the real world without restrictive assumptions. We also provide in-depth analysis and discussions for each module, with the hope that our design tradeoffs can generalize to other related applications. Videos of the complete system can be found at: this https URL.

Includes members:
  Member "improved generalization of heading direction estimation for aerial filming using semi supervised regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments")
  Member "can a robot become a movie director learning artistic principles for aerial cinematography":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "keyfilter aware real time uav object tracking")
  Member "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations")
  Member "autonomous drone cinematographer using artistic principles to create smooth safe occlusion free trajectories for aerial filming":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments")
  Member "integrating kinematics and environment context into deep inverse reinforcement learning for predicting off road vehicle trajectories":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trajectory forecasts in unknown environments conditioned on grid based plans")
  Member "optimizing for aesthetically pleasing quadrotor camera motion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "activemocap optimized drone flight for active human motion capture")
  Member "good semi supervised learning that requires a bad gan":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial code learning for image generation")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)