human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "elaborating on learned demonstrations with temporal logic specifications"):
  Abstract: Most current methods for learning from demonstrations assume that those demonstrations alone are sufficient to learn the underlying task. This is often untrue, especially if extra safety specifications exist which were not present in the original demonstrations. In this paper, we allow an expert to elaborate on their original demonstration with additional specification information using linear temporal logic (LTL). Our system converts LTL specifications into a differentiable loss. This loss is then used to learn a dynamic movement primitive that satisfies the underlying specification, while remaining close to the original demonstration. Further, by leveraging adversarial training, our system learns to robustly satisfy the given LTL specification on unseen inputs, not just those seen in training. We show that our method is expressive enough to work across a variety of common movement specification patterns such as obstacle avoidance, patrolling, keeping steady, and speed limitation. In addition, we show that our system can modify a base demonstration with complex specifications by incrementally composing multiple simpler specifications. We also implement our system on a PR-2 robot to show how a demonstrator can start with an initial (sub-optimal) demonstration, then interactively improve task success by including additional specifications enforced with our differentiable LTL loss.

Includes members:
  Member "specification patterns for robotic missions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from explanation to synthesis compositional program induction for learning from demonstration")
  Member "disentangled relational representations for explaining and learning from demonstration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "diversity aware vehicle motion prediction via latent semantic sampling")
  Member "imitation learning of robot policies by combining language vision and demonstration":
    also connected through other hyperedges:
      (none)
  Member "residual policy learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "brpo batch residual policy optimization")
  Member "guiding policies with language via meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "zero shot task adaptation by homoiconic meta mapping")
  Member "a semantic loss function for deep learning with symbolic knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "interpretable apprenticeship learning with temporal logic specifications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "maximum causal entropy specification inference from demonstrations")
  Member "generative adversarial imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

Question: Please tell me which class the source paper 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)