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: "inverse reinforcement learning with gaussian process"
  Abstract: We present new algorithms for inverse reinforcement learning (IRL, or inverse optimal control) in convex optimization settings. We argue that finite-space IRL can be posed as a convex quadratic program under a Bayesian inference framework with the objective of maximum a posterior estimation. To deal with problems in large or even infinite state space, we propose a Gaussian process model and use preference graphs to represent observations of decision trajectories. Our method is distinguished from other approaches to IRL in that it makes no assumptions about the form of the reward function and yet it retains the promise of computationally manageable implementations for potential real-world applications. In comparison with an establish algorithm on small-scale numerical problems, our method demonstrated better accuracy in apprenticeship learning and a more robust dependence on the number of observations.

Connected through hyperedge HE-1 (induced by source paper "human apprenticeship learning via kernel based inverse reinforcement learning"):
  with members:
    - "first person activity forecasting with online inverse reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "multi agent inverse reinforcement learning for general sum stochastic games"):
  with members:
    - "cooperative inverse reinforcement learning"
    - "decentralized anti coordination through multi agent learning"
    - "computational rationalization the inverse equilibrium problem"
    - "a multiagent reinforcement learning algorithm with non linear dynamics"
    - "awesome a general multiagent learning algorithm that converges in self play and learns a best response against stationary opponents"

Connected through hyperedge HE-3 (induced by source paper "comparison of multi agent and single agent inverse learning on a simulated soccer example"):
  with members:
    - "multi agent inverse reinforcement learning for zero sum games"
    - "computational rationalization the inverse equilibrium problem"
    - "multiagent learning in large anonymous games"
    - "a multiagent reinforcement learning algorithm with non linear dynamics"

Connected through hyperedge HE-4 (induced by source paper "multi agent inverse reinforcement learning for two person zero sum games"):
  with members:
    - "adaptive shooting for bots in first person shooter games using reinforcement learning"
    - "move evaluation in go using deep convolutional neural networks"
    - "a multiagent reinforcement learning algorithm with non linear dynamics"

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)