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 via deep gaussian process"
  Abstract: The report proposes a new approach for inverse reinforcement learning based on deep Gaussian process (GP), which is capable of learning complicated reward structures with few demonstrations. The model stacks multiple latent GP layers to learn abstract representations of the state feature space, which is linked to the demonstrations through the Maximum Entropy learning framework. As analytic derivation of the model evidence is prohibitive due to the nonlinearity of latent variables, variational inference is employed for approximate inference, based on a special choice of variational distributions. This guards the model from over training, achieving the Automatic Occam's Razor. Experiments on the benchmark test, i.e., object world, as well as a new setup, i.e., binary world, are performed, where the proposed method outperforms state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "towards inverse reinforcement learning for limit order book dynamics"):
  with members:
    - "open ended learning in symmetric zero sum games"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "maximum entropy deep inverse reinforcement learning"

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)