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: "active choice of teachers learning strategies and goals for a socially guided intrinsic motivation learner"
  Abstract: We present an active learning architecture that allows a robot to actively learn which data collection strategy is most efficient for acquiring motor skills to achieve multiple outcomes, and generalise over its experience to achieve new outcomes. The robot explores its environment both via interactive learning and goal-babbling. It learns at the same time when, who and what to actively imitate from several available teachers, and learns when not to use social guidance but use active goal-oriented self-exploration. This is formalised in the framework of life-long strategic learning.

Connected through hyperedge HE-1 (induced by source paper "curious intrinsically motivated modular multi goal reinforcement learning"):
  with members:
    - "hierarchical reinforcement learning with hindsight"
    - "data efficient hierarchical reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "unicorn continual learning with a universal off policy agent"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-2 (induced by source paper "accuracy based curriculum learning in deep reinforcement learning"):
  with members:
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "automated curriculum learning for neural networks"
    - "deep reinforcement learning that matters"
    - "hindsight experience replay"
    - "teacher student curriculum learning"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "learning a set of interrelated tasks by using sequences of motor policies for a strategic intrinsically motivated learner"):
  with members:
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "bootstrapping intrinsically motivated learning with human demonstration"

Connected through hyperedge HE-4 (induced by source paper "autonomous development and learning in artificial intelligence and robotics scaling up deep learning to human like learning"):
  with members:
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "unifying count based exploration and intrinsic motivation"
    - "what do we learn about development from baby robots"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"

Connected through hyperedge HE-5 (induced by source paper "diversity driven selection of exploration strategies in multi armed bandits"):
  with members:
    - "multi armed bandits for intelligent tutoring systems"
    - "open ended evolutionary robotics an information theoretic approach"

Connected through hyperedge HE-6 (induced by source paper "open challenges in understanding development and evolution of speech forms the roles of embodied self organization motivation and active exploration"):
  with members:
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "the self organization of speech sounds"

Connected through hyperedge HE-7 (induced by source paper "socially guided intrinsic motivation for robot learning of motor skills"):
  with members:
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "learning parameterized skills"
    - "interactive policy learning through confidence based autonomy"
    - "active learning with statistical models"

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.LG(Machine Learning)