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: "multi task maximum entropy inverse reinforcement learning"
  Abstract: Multi-task Inverse Reinforcement Learning (IRL) is the problem of inferring multiple reward functions from expert demonstrations. Prior work, built on Bayesian IRL, is unable to scale to complex environments due to computational constraints. This paper contributes a formulation of multi-task IRL in the more computationally efficient Maximum Causal Entropy (MCE) IRL framework. Experiments show our approach can perform one-shot imitation learning in a gridworld environment that single-task IRL algorithms need hundreds of demonstrations to solve. We outline preliminary work using meta-learning to extend our method to the function approximator setting of modern MCE IRL algorithms. Evaluating on multi-task variants of common simulated robotics benchmarks, we discover serious limitations of these IRL algorithms, and conclude with suggestions for further work.

Connected through hyperedge HE-1 (induced by source paper "learning to generalize from sparse and underspecified rewards"):
  with members:
    - "self supervised generalisation with meta auxiliary learning"
    - "reward shaping via meta learning"
    - "on first order meta learning algorithms"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "discourse aware neural rewards for coherent text generation"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "learning to teach with dynamic loss functions"

Connected through hyperedge HE-2 (induced by source paper "meta inverse reinforcement learning with probabilistic context variables"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning to optimize neural nets"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "directed info gail learning hierarchical policies from unsegmented demonstrations using directed information"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"

Connected through hyperedge HE-3 (induced by source paper "on the feasibility of learning rather than assuming human biases for reward inference"):
  with members:
    - "universal planning networks"
    - "learning to search with mctsnets"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "machine theory of mind"
    - "where do you think you re going inferring beliefs about dynamics from behavior"
    - "learning model based planning from scratch"
    - "occam s razor is insufficient to infer the preferences of irrational agents"
    - "inverse reward design"

Connected through hyperedge HE-4 (induced by source paper "few shot goal inference for visuomotor learning and planning"):
  with members:
    - "universal planning networks"
    - "reward learning from narrated demonstrations"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "self supervised visual planning with temporal skip connections"
    - "one shot visual imitation learning via meta learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"

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)