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 "a robot that learns connect four using game theory and demonstrations"):
  Abstract: Teaching robots new skills using minimal time and effort has long been a goal of artificial intelligence. This paper investigates the use of game theoretic representations to represent and learn how to play interactive games such as Connect Four. We combine aspects of learning by demonstration, active learning, and game theory allowing a robot to learn by presenting its understanding of the structure of the game and conducting a question/answer session with a person. The paper demonstrates how a robot can be taught the win conditions of the game Connect Four and its variants using a single demonstration and a few trial examples with a question and answer session led by the robot. Our results show that the robot can learn any arbitrary win conditions for the Connect Four game without any prior knowledge of the win conditions and then play the game with a human utilizing the learned win conditions. Our experiments also show that some questions are more important for learning the game's win conditions.

Includes members:
  Member "few shot learning with meta metric learners":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a hybrid approach with optimization and metric based meta learner for few shot learning")
  Member "are few shot learning benchmarks too simple":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locality and compositionality in zero shot learning")
  Member "centroid networks for few shot clustering and unsupervised few shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "locality and compositionality in zero shot learning")
  Member "meta learning for semi supervised few shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "one shot hierarchical imitation learning of compound visuomotor tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transferable task execution from pixels through deep planning domain learning")
  Member "mastering chess and shogi by self play with a general reinforcement learning algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "learning game representations from data using rationality constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "predicting human behavior in unrepeated simultaneous move games")

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.RO(Robotics)