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: "goal inference improves objective and perceived performance in human robot collaboration"
  Abstract: The study of human-robot interaction is fundamental to the design and use of robotics in real-world applications. Robots will need to predict and adapt to the actions of human collaborators in order to achieve good performance and improve safety and end-user adoption. This paper evaluates a human-robot collaboration scheme that combines the task allocation and motion levels of reasoning: the robotic agent uses Bayesian inference to predict the next goal of its human partner from his or her ongoing motion, and re-plans its own actions in real time. This anticipative adaptation is desirable in many practical scenarios, where humans are unable or unwilling to take on the cognitive overhead required to explicitly communicate their intent to the robot. A behavioral experiment indicates that the combination of goal inference and dynamic task planning significantly improves both objective and perceived performance of the human-robot team. Participants were highly sensitive to the differences between robot behaviors, preferring to work with a robot that adapted to their actions over one that did not.

Connected through hyperedge HE-1 (induced by source paper "convergence of learning dynamics in stackelberg games"):
  with members:
    - "on gradient based learning in continuous games"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "on the convergence of gradient based learning in continuous games"
    - "the mechanics of n player differentiable games"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"

Connected through hyperedge HE-2 (induced by source paper "towards better human robot collaboration with robust plan recognition and trajectory prediction"):
  with members:
    - "interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory"
    - "human motion prediction using semi adaptable neural networks"
    - "human activity recognition in rgb d videos by dynamic images"
    - "robot safe interaction system for intelligent industrial co robots"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "courteous autonomous cars"
    - "learning human motion models for long term predictions"

Connected through hyperedge HE-3 (induced by source paper "on the manipulation of articulated objects in human robot cooperation scenarios"):
  with members:
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "ai challenges in human robot cognitive teaming"
    - "flexible human robot cooperation models for assisted shop floor tasks"
    - "a description logic primer"
    - "modelling mixed discrete continuous domains for planning"
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"
    - "the symbol grounding problem"

Connected through hyperedge HE-4 (induced by source paper "robot planning with mathematical models of human state and action"):
  with members:
    - "generating plans that predict themselves"
    - "expressive robot motion timing"
    - "should robots be obedient"
    - "enabling robots to communicate their objectives"
    - "cooperative inverse reinforcement learning"
    - "the off switch game"

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)