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: "learning of behavior trees for autonomous agents"
  Abstract: Definition of an accurate system model for Automated Planner (AP) is often impractical, especially for real-world problems. Conversely, off-the-shelf planners fail to scale up and are domain dependent. These drawbacks are inherited from conventional transition systems such as Finite State Machines (FSMs) that describes the action-plan execution generated by the AP. On the other hand, Behavior Trees (BTs) represent a valid alternative to FSMs presenting many advantages in terms of modularity, reactiveness, scalability and domain-independence. In this paper, we propose a model-free AP framework using Genetic Programming (GP) to derive an optimal BT for an autonomous agent to achieve a given goal in unknown (but fully observable) environments. We illustrate the proposed framework using experiments conducted with an open source benchmark Mario AI for automated generation of BTs that can play the game character Mario to complete a certain level at various levels of difficulty to include enemies and obstacles.

Connected through hyperedge HE-1 (induced by source paper "integrating acting planning and learning in hierarchical operational models"):
  with members:
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "learning in pomdps with monte carlo tree search"
    - "towards deep symbolic reinforcement learning"
    - "reinforcement learning a survey"

Connected through hyperedge HE-2 (induced by source paper "automated generation of reactive programs from human demonstration for orchestration of robot behaviors"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "an algorithmic perspective on imitation learning"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-3 (induced by source paper "automatic difficulty management and testing in games using a framework based on behavior trees and genetic algorithms"):
  with members:
    - "faster algorithms for the constrained k means problem"
    - "transfer learning across networks for collective classification"

Connected through hyperedge HE-4 (induced by source paper "adding neural network controllers to behavior trees without destroying performance guarantees"):
  with members:
    - "machine learning and evolutionary techniques in interplanetary trajectory design"
    - "behavior trees in robotics and ai an introduction"
    - "a lyapunov based approach to safe reinforcement learning"
    - "deep learning for video game playing"
    - "simulation results on selector adaptation in behavior trees"
    - "costar instructing collaborative robots with behavior trees and vision"
    - "a neural algorithm of artistic style"
    - "a framework for constrained and adaptive behavior based agents"

Connected through hyperedge HE-5 (induced by source paper "improving the modularity of auv control systems using behaviour trees"):
  with members:
    - "3d pursuit evasion for auvs"
    - "behavior trees in robotics and ai an introduction"
    - "adding neural network controllers to behavior trees without destroying performance guarantees"
    - "a two step pursuit evasion algorithm for autonomous underwater vehicles"
    - "towards blended reactive planning and acting using behavior trees"
    - "costar instructing collaborative robots with behavior trees and vision"

Connected through hyperedge HE-6 (induced by source paper "behavior trees as a representation for medical procedures"):
  with members:
    - "costar instructing collaborative robots with behavior trees and vision"

Connected through hyperedge HE-7 (induced by source paper "towards blended reactive planning and acting using behavior trees"):
  with members:
    - "behavior trees in robotics and ai an introduction"
    - "improving the modularity of auv control systems using behaviour trees"
    - "adding neural network controllers to behavior trees without destroying performance guarantees"
    - "the ff planning system fast plan generation through heuristic search"

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)