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: "computational theories of curiosity driven learning"
  Abstract: What are the functions of curiosity? What are the mechanisms of curiosity-driven learning? We approach these questions using concepts and tools from machine learning and developmental robotics. We argue that curiosity-driven learning enables organisms to make discoveries to solve complex problems with rare or deceptive rewards. By fostering exploration and discovery of a diversity of behavioural skills, and ignoring these rewards, curiosity can be efficient to bootstrap learning when there is no information, or deceptive information, about local improvement towards these problems. We review both normative and heuristic computational frameworks used to understand the mechanisms of curiosity in humans, conceptualizing the child as a sense-making organism. These frameworks enable us to discuss the bi-directional causal links between curiosity and learning, and to provide new hypotheses about the fundamental role of curiosity in self-organizing developmental structures through curriculum learning. We present various developmental robotics experiments that study these mechanisms in action, both supporting these hypotheses and opening new research avenues in machine learning and artificial intelligence. Finally, we discuss challenges for the design of experimental paradigms for studying curiosity in psychology and cognitive neuroscience. Keywords: Curiosity, intrinsic motivation, lifelong learning, predictions, world model, rewards, free-energy principle, learning progress, machine learning, AI, developmental robotics, development, curriculum learning, self-organization.

Connected through hyperedge HE-1 (induced by source paper "meta learning curiosity algorithms"):
  with members:
    - "evolving loss functions with multivariate taylor polynomial parameterizations"
    - "few shot bayesian imitation learning with logical program policies"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "self supervised exploration via disagreement"
    - "few shot bayesian imitation learning with logic over programs"
    - "discovery of useful questions as auxiliary tasks"
    - "world discovery models"
    - "weight agnostic neural networks"

Connected through hyperedge HE-2 (induced by source paper "self paced contextual reinforcement learning"):
  with members:
    - "accuracy based curriculum learning in deep reinforcement learning"
    - "temporal difference models model free deep rl for model based control"
    - "learning curriculum policies for reinforcement learning"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "hindsight experience replay"
    - "automatic goal generation for reinforcement learning agents"
    - "reverse curriculum generation for reinforcement learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-3 (induced by source paper "computational and robotic models of early language development a review"):
  with members:
    - "cognitive science in the era of artificial intelligence a roadmap for reverse engineering the infant language learner"
    - "kauffman s adjacent possible in word order evolution"

Connected through hyperedge HE-4 (induced by source paper "automated curriculum learning by rewarding temporally rare events"):
  with members:
    - "hindsight experience replay"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "autoencoder augmented neuroevolution for visual doom playing"
    - "deep learning for video game playing"
    - "starcraft ii a new challenge for reinforcement learning"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-5 (induced by source paper "curiosity driven exploration of learned disentangled goal spaces"):
  with members:
    - "zero shot visual imitation"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "hindsight experience replay"
    - "independently controllable factors"
    - "reverse curriculum generation for reinforcement learning"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "intrinsically motivated goal exploration processes with automatic curriculum learning"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-6 (induced by source paper "large scale study of curiosity driven learning"):
  with members:
    - "zero shot visual imitation"
    - "parameter space noise for exploration"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "intrinsic motivation and automatic curricula via asymmetric self play"
    - "count based exploration with neural density models"
    - "surprise based intrinsic motivation for deep reinforcement learning"
    - "ucb and infogain exploration via boldsymbol q ensembles"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-7 (induced by source paper "model based active exploration"):
  with members:
    - "self supervised exploration via disagreement"
    - "model ensemble trust region policy optimization"
    - "large scale study of curiosity driven learning"
    - "exploration by distributional reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "curiosity driven exploration by self supervised prediction"

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.AI(Artificial Intelligence)