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 "using logical specifications of objectives in multi objective reinforcement learning"):
  Abstract: In the multi-objective reinforcement learning (MORL) paradigm, the relative importance of each environment objective is often unknown prior to training, so agents must learn to specialize their behavior to optimize different combinations of environment objectives that are specified post-training. These are typically linear combinations, so the agent is effectively parameterized by a weight vector that describes how to balance competing environment objectives. However, many real world behaviors require non-linear combinations of objectives. Additionally, the conversion between desired behavior and weightings is often unclear. #R##N#In this work, we explore the use of a language based on propositional logic with quantitative semantics--in place of weight vectors--for specifying non-linear behaviors in an interpretable way. We use a recurrent encoder to encode logical combinations of objectives, and train a MORL agent to generalize over these encodings. We test our agent in several grid worlds with various objectives and show that our agent can generalize to many never-before-seen specifications with performance comparable to single policy baseline agents. We also demonstrate our agent's ability to generate meaningful policies when presented with novel specifications and quickly specialize to novel specifications.

Includes members:
  Member "using natural language for reward shaping in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration")
  Member "temporal logic guided safe reinforcement learning using control barrier functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safety considerations in deep control policies with safety barrier certificates under uncertainty")
  Member "from language to goals inverse reinforcement learning for vision based instruction following":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration")
  Member "dynamic weights in multi objective deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "motiac multi objective actor critics for real time bidding")
  Member "soft actor critic algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ready policy one world building through active learning")
  Member "generalizing across multi objective reward functions in deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi objective multi agent decision making a utility based analysis and survey")
  Member "hindsight experience replay":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "q learning for robust satisfaction of signal temporal logic specifications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "tractable reinforcement learning of signal temporal logic objectives")

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)