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: "high speed flight in an ergodic forest"
  Abstract: Inspired by birds flying through cluttered environments such as dense forests, this paper studies the theoretical foundations of high-speed motion through a randomly-generated obstacle field. Assuming that the locations and the sizes of the trees are determined by an ergodic point process, and under mild technical conditions on the dynamics of the bird, it is shown that the existence of an infinite collision-free trajectory through the forest exhibits a phase transition. In other words, if the bird flies faster than a certain critical speed, there is no infinite collision-free trajectory, with probability one, i.e., the bird will eventually collide with some tree, almost surely, regardless of the planning algorithm governing its motion. On the other hand, if the bird flies slower than this critical speed, then there exists at least one infinite collision-free trajectory, almost surely. Lower and upper bounds on the critical speed are derived for the special case of a Poisson forest considering a simple model for the bird's dynamics. Moreover, results from an extensive Monte-Carlo simulation study are presented. This paper also establishes novel connections between robot motion planning and statistical physics through ergodic theory and the theory of percolation, which may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "reactive navigation in partially familiar planar environments using semantic perceptual feedback"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "conceptualization of object compositions using persistent homology"
    - "admissible abstractions for near optimal task and motion planning"
    - "visual memory for robust path following"
    - "yolov3 an incremental improvement"
    - "sampling based methods for factored task and motion planning"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "6 dof object pose from semantic keypoints"

Connected through hyperedge HE-2 (induced by source paper "technical report reactive semantic planning in unexplored semantic environments using deep perceptual feedback"):
  with members:
    - "reactive navigation in partially familiar planar environments using semantic perceptual feedback"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "neural autonomous navigation with riemannian motion policy"
    - "yolov3 an incremental improvement"
    - "semi parametric topological memory for navigation"
    - "6 dof object pose from semantic keypoints"

Connected through hyperedge HE-3 (induced by source paper "pretrained ai models performativity mobility and change"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "on measuring social biases in sentence encoders"
    - "spanbert improving pre training by representing and predicting spans"
    - "patentbert patent classification with fine tuning a pre trained bert model"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "the ethics of ai ethics an evaluation of guidelines"
    - "end to end open domain question answering with"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-4 (induced by source paper "autonomous navigation of mavs in unknown cluttered environments"):
  with members:
    - "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"
    - "nanomap fast uncertainty aware proximity queries with lazy search over local 3d data"
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "real time trajectory replanning for mavs using uniform b splines and a 3d circular buffer"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-5 (induced by source paper "distributed attack robust submodular maximization for multi robot planning"):
  with members:
    - "the impact of information in distributed submodular maximization"
    - "distributed state estimation using intermittently connected robot networks"
    - "robust monotone submodular function maximization"
    - "resilient active information gathering with mobile robots"
    - "resilient active target tracking with multiple robots"
    - "distributed submodular maximization with limited information"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "fast algorithms for the maximum clique problem on massive sparse graphs"

Connected through hyperedge HE-6 (induced by source paper "a complete system for vision based micro aerial vehicle mapping planning and flight in cluttered environments"):
  with members:
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "sparse 3d topological graphs for micro aerial vehicle planning"
    - "c blox a scalable and consistent tsdf based dense mapping approach"
    - "real time trajectory replanning for mavs using uniform b splines and a 3d circular buffer"
    - "nonlinear model predictive control for multi micro aerial vehicle robust collision avoidance"

Connected through hyperedge HE-7 (induced by source paper "resilient active information gathering with mobile robots"):
  with members:
    - "robust monotone submodular function maximization"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "information acquisition with sensing robots algorithms and error bounds"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"

Connected through hyperedge HE-8 (induced by source paper "learning heuristic search via imitation"):
  with members:
    - "adaptive information gathering via imitation learning"
    - "learning from the hindsight plan episodic mpc improvement"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
    - "value iteration networks"
    - "learning to gather information via imitation"
    - "prioritized experience replay"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"

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)