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: "bootstrapping intrinsically motivated learning with human demonstration"
  Abstract: This paper studies the coupling of internally guided learning and social interaction, and more specifically the improvement owing to demonstrations of the learning by intrinsic motivation. We present Socially Guided Intrinsic Motivation by Demonstration (SGIM-D), an algorithm for learning in continuous, unbounded and non-preset environments. After introducing social learning and intrinsic motivation, we describe the design of our algorithm, before showing through a fishing experiment that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation to gain a wide repertoire while being specialised in specific subspaces.

Connected through hyperedge HE-1 (induced by source paper "clic curriculum learning and imitation for object control in non rewarding environments"):
  with members:
    - "learning actionable representations with goal conditioned policies"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "large scale study of curiosity driven learning"
    - "learning curriculum policies for reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"
    - "many goals reinforcement learning"
    - "curious intrinsically motivated modular multi goal reinforcement learning"

Connected through hyperedge HE-2 (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"
    - "active choice of teachers learning strategies and goals for a socially guided intrinsic motivation learner"

Connected through hyperedge HE-3 (induced by source paper "active learning for autonomous intelligent agents exploration curiosity and interaction"):
  with members:
    - "multi class generalized binary search for active inverse reinforcement learning"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "selecting the state representation in reinforcement learning"
    - "local utility elicitation in gai models"
    - "gaussian processes for sample efficient reinforcement learning with rmax like exploration"
    - "variance based rewards for approximate bayesian reinforcement learning"
    - "near optimal nonmyopic value of information in graphical models"
    - "safe exploration in markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "active learning of inverse models with intrinsically motivated goal exploration in robots"):
  with members:
    - "path integral policy improvement with covariance matrix adaptation"
    - "learning parameterized skills"
    - "powerplay training an increasingly general problem solver by continually searching for the simplest still unsolvable problem"
    - "interactive policy learning through confidence based autonomy"
    - "active learning with statistical models"

Connected through hyperedge HE-5 (induced by source paper "active choice of teachers learning strategies and goals for a socially guided intrinsic motivation learner"):
  with members:
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "learning parameterized skills"
    - "interactive policy learning through confidence based autonomy"

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)