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: "interpretable apprenticeship learning with temporal logic specifications"
  Abstract: Recent work has addressed using formulas in linear temporal logic (LTL) as specifications for agents planning in Markov Decision Processes (MDPs). We consider the inverse problem: inferring an LTL specification from demonstrated behavior trajectories in MDPs. We formulate this as a multiobjective optimization problem, and describe state-based ("what actually happened") and action-based ("what the agent expected to happen") objective functions based on a notion of "violation cost". We demonstrate the efficacy of the approach by employing genetic programming to solve this problem in two simple domains.

Connected through hyperedge HE-1 (induced by source paper "elaborating on learned demonstrations with temporal logic specifications"):
  with members:
    - "specification patterns for robotic missions"
    - "disentangled relational representations for explaining and learning from demonstration"
    - "imitation learning of robot policies by combining language vision and demonstration"
    - "residual policy learning"
    - "guiding policies with language via meta learning"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "generative adversarial imitation learning"
    - "robust linear temporal logic"

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"
    - "learning task specifications from demonstrations"
    - "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"
    - "learning task specifications from demonstrations"
    - "environment independent task specifications via gltl"

Connected through hyperedge HE-4 (induced by source paper "planning with state abstractions for non markovian task specifications"):
  with members:
    - "evaluating compositionality in sentence embeddings"
    - "accurately and efficiently interpreting human robot instructions of varying granularities"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "six challenges for neural machine translation"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "constructing abstraction hierarchies using a skill symbol loop"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "learning task specifications from demonstrations via the principle of maximum causal entropy"):
  with members:
    - "a model counter s guide to probabilistic systems"
    - "composable deep reinforcement learning for robotic manipulation"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "spudd stochastic planning using decision diagrams"

Connected through hyperedge HE-6 (induced by source paper "automata guided reinforcement learning with demonstrations"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "composable deep reinforcement learning for robotic manipulation"
    - "learning dexterous in hand manipulation"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "taco learning task decomposition via temporal alignment for control"
    - "deep reinforcement learning for dexterous manipulation with concept networks"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"

Connected through hyperedge HE-7 (induced by source paper "specification inference from demonstrations"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "environment independent task specifications via gltl"
    - "concrete problems in ai safety"
    - "guided cost learning deep inverse optimal control via policy optimization"

Connected through hyperedge HE-8 (induced by source paper "norm conflict resolution in stochastic domains"):
  with members:
    - "robust satisfaction of temporal logic specifications via reinforcement learning"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "mdp optimal control under temporal logic constraints"
    - "on graphical modeling of preference and importance"

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.SY(Systems and Control)