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 "grounding natural language commands to starcraft ii game states for narration guided reinforcement learning"):
  Abstract: While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniques are still susceptible to the longstanding problem of {\em reward sparsity}. This is especially true for tasks such as training an agent to play StarCraft II, a real-time strategy game where reward is only given at the end of a game which is usually very long. While this problem can be addressed through reward shaping, such approaches typically require a human expert with specialized knowledge. Inspired by the vision of enabling reward shaping through the more-accessible paradigm of natural-language narration, we investigate to what extent we can contextualize these narrations by grounding them to the goal-specific states. We present a mutual-embedding model using a multi-input deep-neural network that projects a sequence of natural language commands into the same high-dimensional representation space as corresponding goal states. We show that using this model we can learn an embedding space with separable and distinct clusters that accurately maps natural-language commands to corresponding game states . We also discuss how this model can allow for the use of narrations as a robust form of reward shaping to improve RL performance and efficiency.

Includes members:
  Member "efficiently combining human demonstrations and interventions for safe training of autonomous systems in real time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning from interventions using hierarchical policies for safe learning")
  Member "on reinforcement learning for full length game of starcraft":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep reinforcement learning for cyber security")
  Member "follownet robot navigation by following natural language directions with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep compositional robotic planners that follow natural language commands")
  Member "following high level navigation instructions on a simulated quadcopter with imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "affect based intrinsic rewards for exploration and learning")
  Member "reward learning from narrated demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a model of fast concept inference with object factorized cognitive programs")
  Member "accurately and efficiently interpreting human robot instructions of varying granularities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using natural language for reward shaping in reinforcement learning")
  Member "starcraft ii a new challenge for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi agent reinforcement learning with emergent roles")
  Member "deep tamer interactive agent shaping in high dimensional state spaces":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fresh interactive reward shaping in high dimensional state spaces using human feedback")

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.MM(Multimedia)