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: "maximum entropy deep inverse reinforcement learning"
  Abstract: This paper presents a general framework for exploiting the representational capacity of neural networks to approximate complex, nonlinear reward functions in the context of solving the inverse reinforcement learning (IRL) problem. We show in this context that the Maximum Entropy paradigm for IRL lends itself naturally to the efficient training of deep architectures. At test time, the approach leads to a computational complexity independent of the number of demonstrations, which makes it especially well-suited for applications in life-long learning scenarios. Our approach achieves performance commensurate to the state-of-the-art on existing benchmarks while exceeding on an alternative benchmark based on highly varying reward structures. Finally, we extend the basic architecture - which is equivalent to a simplified subclass of Fully Convolutional Neural Networks (FCNNs) with width one - to include larger convolutions in order to eliminate dependency on precomputed spatial features and work on raw input representations.

Connected through hyperedge HE-1 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning"):
  with members:
    - "analysing user identity via time sensitive semantic edit distance t sed a case study of russian trolls on twitter"
    - "still out there modeling and identifying russian troll accounts on twitter"
    - "red bots do it better comparative analysis of social bot partisan behavior"
    - "arming the public with artificial intelligence to counter social bots"
    - "senator we sell ads analysis of the 2016 russian facebook ads campaign"
    - "characterizing the 2016 russian ira influence campaign"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"

Connected through hyperedge HE-2 (induced by source paper "trajectory forecasts in unknown environments conditioned on grid based plans"):
  with members:
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "conditional flow variational autoencoders for structured sequence prediction"
    - "scene compliant trajectory forecast with agent centric spatio temporal grids"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "convolutional social pooling for vehicle trajectory prediction"

Connected through hyperedge HE-3 (induced by source paper "joint goal and strategy inference across heterogeneous demonstrators via reward network distillation"):
  with members:
    - "distilling policy distillation"
    - "learning novel policies for tasks"
    - "ranking based reward extrapolation without rankings"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "repeated inverse reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "scalable multi task imitation learning with autonomous improvement"):
  with members:
    - "unsupervised visuomotor control through distributional planning networks"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "one shot high fidelity imitation training large scale deep nets with rl"
    - "soft actor critic algorithms and applications"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "task embedded control networks for few shot imitation learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "hindsight experience replay"

Connected through hyperedge HE-5 (induced by source paper "learning robotic ultrasound scanning using probabilistic temporal ranking"):
  with members:
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "rapid probabilistic interest learning from domain specific pairwise image comparisons"
    - "a divergence minimization perspective on imitation learning methods"
    - "composing diverse policies for temporally extended tasks"
    - "generative adversarial imitation learning"
    - "informative path planning and mapping with multiple uavs in wind fields"

Connected through hyperedge HE-6 (induced by source paper "active task inference guided deep inverse reinforcement learning"):
  with members:
    - "taco learning task decomposition via temporal alignment for control"
    - "hirl hierarchical inverse reinforcement learning for long horizon tasks with delayed rewards"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "apprenticeship learning using inverse reinforcement learning and gradient methods"

Connected through hyperedge HE-7 (induced by source paper "less is more rethinking probabilistic models of human behavior"):
  with members:
    - "learning under misspecified objective spaces"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-8 (induced by source paper "pixelrl fully convolutional network with reinforcement learning for image processing"):
  with members:
    - "weakly supervised video summarization by hierarchical reinforcement learning"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "ffnet video fast forwarding via reinforcement learning"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "synthesizing programs for images using reinforced adversarial 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)