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: "behavior trees as a representation for medical procedures"
  Abstract: Objective: Effective collaboration between machines and clinicians requires flexible data structures to represent medical processes and clinical practice guidelines. Such a data structure could enable effective turn-taking between human and automated components of a complex treatment, accurate on-line monitoring of clinical treatments (for example to detect medical errors), or automated treatment systems (such as future medical robots) whose overall treatment plan is understandable and auditable by human experts. #R##N#Materials and Methods: Behavior trees (BTs) emerged from video game development as a graphical language for modeling intelligent agent behavior. BTs have several properties which are attractive for modeling medical procedures including human-readability, authoring tools, and composability. #R##N#Results: This paper will illustrate construction of BTs for exemplary medical procedures and clinical protocols. #R##N#Discussion and Conclusion: Behavior Trees thus form a useful, and human authorable/readable bridge between clinical practice guidelines and AI systems.

Connected through hyperedge HE-1 (induced by source paper "representing robot task plans as robust logical dynamical systems"):
  with members:
    - "behavior trees in robotics and ai an introduction"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "evaluating methods for end user creation of robot task plans"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "costar instructing collaborative robots with behavior trees and vision"

Connected through hyperedge HE-2 (induced by source paper "hidden markov models derived from behavior trees"):
  with members:
    - "costar instructing collaborative robots with behavior trees and vision"

Connected through hyperedge HE-3 (induced by source paper "improving the parallel execution of behavior trees"):
  with members:
    - "behavior trees in robotics and ai an introduction"
    - "teleo reactive programs for agent control"

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.HC(Human-Computer Interaction)