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: "a survey of inverse reinforcement learning challenges methods and progress"
  Abstract: Inverse reinforcement learning is the problem of inferring the reward function of an observed agent, given its policy or behavior. Researchers perceive IRL both as a problem and as a class of methods. By categorically surveying the current literature in IRL, this article serves as a reference for researchers and practitioners in machine learning to understand the challenges of IRL and select the approaches best suited for the problem on hand. The survey formally introduces the IRL problem along with its central challenges which include accurate inference, generalizability, correctness of prior knowledge, and growth in solution complexity with problem size. The article elaborates how the current methods mitigate these challenges. We further discuss the extensions of traditional IRL methods: (i) inaccurate and incomplete perception, (ii) incomplete model, (iii) multiple rewards, and (iv) non-linear reward functions. This discussion concludes with some broad advances in the research area and currently open research questions.

Connected through hyperedge HE-1 (induced by source paper "better than demonstrator imitation learning via automatically ranked demonstrations"):
  with members:
    - "inverse reinforcement learning with multiple ranked experts"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "imitation learning from imperfect demonstration"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "risk aware active inverse reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "an algorithmic perspective on imitation learning"
    - "ensembledagger a bayesian approach to safe imitation learning"

Connected through hyperedge HE-2 (induced by source paper "energy based continuous inverse optimal control"):
  with members:
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "simulating emergent properties of human driving behavior using multi agent reward augmented imitation learning"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "simplifying reward design through divide and conquer"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"

Connected through hyperedge HE-3 (induced by source paper "composing diverse policies for temporally extended tasks"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "using causal analysis to learn specifications from task demonstrations"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "deepmdp learning continuous latent space models for representation learning"
    - "from explanation to synthesis compositional program induction for learning from demonstration"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "learning safe policies with expert guidance"

Connected through hyperedge HE-4 (induced by source paper "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "inverse reinforcement learning for video games"
    - "one shot learning of multi step tasks from observation via activity localization in auxiliary video"
    - "playing hard exploration games by watching youtube"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "generative adversarial imitation from observation"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "behavioral cloning from observation"

Connected through hyperedge HE-5 (induced by source paper "deep bayesian reward learning from preferences"):
  with members:
    - "asking easy questions a user friendly approach to active reward learning"
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "beyond confidence regions tight bayesian ambiguity sets for robust mdps"
    - "risk aware active inverse reinforcement learning"
    - "importance sampling policy evaluation with an estimated behavior policy"
    - "playing hard exploration games by watching youtube"
    - "risk sensitive generative adversarial imitation learning"

Connected through hyperedge HE-6 (induced by source paper "there is no artificial general intelligence"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "neural approaches to conversational ai"
    - "a style based generator architecture for generative adversarial networks"
    - "a knowledge grounded neural conversation model"
    - "learned in translation contextualized word vectors"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "measuring the tendency of cnns to learn surface statistical regularities"

Connected through hyperedge HE-7 (induced by source paper "scenario generalization of data driven imitation models in crowd simulation"):
  with members:
    - "theoretical perspective of deep domain adaptation"
    - "on deep domain adaptation some theoretical understandings"
    - "behavioral cloning from observation"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "the role of data driven priors in multi agent crowd trajectory estimation"
    - "imitating driver behavior with generative adversarial networks"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-8 (induced by source paper "federated imitation learning a novel framework for cloud robotic systems with heterogeneous sensor data"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "a comprehensive survey on transfer learning"
    - "meta learning a survey"
    - "roboturk a crowdsourcing platform for robotic skill learning through imitation"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "one shot visual imitation learning via meta learning"
    - "overcoming exploration in reinforcement learning with demonstrations"
    - "federated learning of deep networks using model averaging"

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)