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: "learning task specifications from demonstrations"
  Abstract: Real world applications often naturally decompose into several sub-tasks. In many settings (e.g., robotics) demonstrations provide a natural way to specify the sub-tasks. However, most methods for learning from demonstrations either do not provide guarantees that the artifacts learned for the sub-tasks can be safely recombined or limit the types of composition available. Motivated by this deficit, we consider the problem of inferring Boolean non-Markovian rewards (also known as logical trace properties or specifications) from demonstrations provided by an agent operating in an uncertain, stochastic environment. Crucially, specifications admit well-defined composition rules that are typically easy to interpret. In this paper, we formulate the specification inference task as a maximum a posteriori (MAP) probability inference problem, apply the principle of maximum entropy to derive an analytic demonstration likelihood model and give an efficient approach to search for the most likely specification in a large candidate pool of specifications. In our experiments, we demonstrate how learning specifications can help avoid common problems that often arise due to ad-hoc reward composition.

Connected through hyperedge HE-1 (induced by source paper "adaptive teaching of temporal logic formulas to learners with preferences"):
  with members:
    - "transfer of temporal logic formulas in reinforcement learning"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "information guided temporal logic inference with prior knowledge"
    - "mining parametric temporal logic properties in model based design for cyber physical systems"
    - "census signal temporal logic inference for multiagent group behavior analysis"
    - "teaching categories to human learners with visual explanations"
    - "learning linear temporal properties"
    - "machine teaching for inverse reinforcement learning algorithms and applications"

Connected through hyperedge HE-2 (induced by source paper "maximum causal entropy specification inference from demonstrations"):
  with members:
    - "a model counter s guide to probabilistic systems"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "interpretable apprenticeship learning with temporal logic specifications"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "spudd stochastic planning using decision diagrams"
    - "on directed information and gambling"

Connected through hyperedge HE-3 (induced by source paper "interactive robot training for non markov tasks"):
  with members:
    - "planning with uncertain specifications puns"
    - "planning with state abstractions for non markovian task specifications"
    - "a survey of reinforcement learning informed by natural language"
    - "batch active preference based learning of reward functions"
    - "environment independent task specifications via gltl"
    - "interpretable apprenticeship learning with temporal logic specifications"

Connected through hyperedge HE-4 (induced by source paper "transfer of temporal logic formulas in reinforcement learning"):
  with members:
    - "information guided temporal logic inference with prior knowledge"
    - "mining parametric temporal logic properties in model based design for cyber physical systems"
    - "census signal temporal logic inference for multiagent group behavior analysis"
    - "learning linear temporal properties"
    - "safe reinforcement learning via shielding"
    - "q learning for robust satisfaction of signal temporal logic specifications"
    - "reinforcement learning with temporal logic rewards"
    - "probably approximately correct mdp learning and control with temporal logic constraints"

Connected through hyperedge HE-5 (induced by source paper "graph temporal logic inference for classification and identification"):
  with members:
    - "information guided temporal logic inference with prior knowledge"
    - "mining parametric temporal logic properties in model based design for cyber physical systems"
    - "census signal temporal logic inference for multiagent group behavior analysis"
    - "learning linear temporal properties"
    - "the robotarium a remotely accessible swarm robotics research testbed"

Connected through hyperedge HE-6 (induced by source paper "a model counter s guide to probabilistic systems"):
  with members:
    - "sequential circuits from regular expressions revisited"
    - "a storm is coming a modern probabilistic model checker"
    - "a scalable approximate model counter"
    - "optimal discrete uniform generation from coin flips and applications"

Connected through hyperedge HE-7 (induced by source paper "maximum likelihood constraint inference for inverse reinforcement learning"):
  with members:
    - "inferring geometric constraints in human demonstrations"
    - "modeling supervisor safe sets for improving collaboration in human robot teams"
    - "learning constraints from demonstrations"
    - "concrete problems in ai safety"

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)