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: "energy based continuous inverse optimal control"
  Abstract: The problem of continuous optimal control (over finite time horizon) is to minimize a given cost function over the sequence of continuous control variables. The problem of continuous inverse optimal control is to learn the unknown cost function from expert demonstrations. In this article, we study this fundamental problem in the framework of energy-based model, where the observed expert trajectories are assumed to be random samples from a probability density function defined as the exponential of the negative cost function up to a normalizing constant. The parameters of the cost function are learned by maximum likelihood via an "analysis by synthesis" scheme, which iterates the following two steps: (1) Synthesis step: sample the synthesized trajectories from the current probability density using the Langevin dynamics. (2) Analysis step: update the model parameters based on the difference between the synthesized trajectories and the observed trajectories. Given the fact that an efficient optimization algorithm is usually available for an optimal control problem, we also consider a variation of the above learning method, where we modify the synthesis step (1) into an optimization step while keeping the analysis step (2) unchanged. Specifically, instead of sampling each synthesized trajectory from the current probability density, we minimize the current cost function over the sequence of control variables using the existing optimization algorithm. We give justifications for this optimization-based method. We demonstrate the proposed energy-based continuous optimal control methods on autonomous driving tasks, and show that the proposed methods can learn suitable cost functions for optimal control.

Connected through hyperedge HE-1 (induced by source paper "integrating planning and interpretable goal recognition for autonomous driving"):
  with members:
    - "when humans aren t optimal robots that collaborate with risk aware humans"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "learning trajectory prediction with continuous inverse optimal control via langevin sampling of energy based models"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "belief and truth in hypothesised behaviours"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "efficient planning under uncertainty with macro actions"

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.LG(Machine Learning)