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: "information driven self organization of complex robotic behaviors"
  Abstract: Information theory is a powerful tool to express principles to drive autonomous systems because it is domain invariant and allows for an intuitive interpretation. This paper studies the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process as a driving force to generate behavior. We study nonlinear and nonstationary systems and introduce the time-local predicting information (TiPI) which allows us to derive exact results together with explicit update rules for the parameters of the controller in the dynamical systems framework. In this way the information principle, formulated at the level of behavior, is translated to the dynamics of the synapses. We underpin our results with a number of case studies with high-dimensional robotic systems. We show the spontaneous cooperativity in a complex physical system with decentralized control. Moreover, a jointly controlled humanoid robot develops a high behavioral variety depending on its physics and the environment it is dynamically embedded into. The behavior can be decomposed into a succession of low-dimensional modes that increasingly explore the behavior space. This is a promising way to avoid the curse of dimensionality which hinders learning systems to scale well.

Connected through hyperedge HE-1 (induced by source paper "an information theoretic on line learning principle for specialization in hierarchical decision making systems"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "systems of bounded rational agents with information theoretic constraints"
    - "an information theoretic on line update principle for perception action coupling"
    - "bounded rational decision making with adaptive neural network priors"
    - "divide and conquer reinforcement learning"
    - "constrained policy optimization"
    - "an information theoretic optimality principle for deep reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "autonomous identification and goal directed invocation of event predictive behavioral primitives"):
  with members:
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning planning and control in a monolithic neural event inference architecture"
    - "intrinsically motivated goal exploration processes with automatic curriculum learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "novel plasticity rule can explain the development of sensorimotor intelligence"

Connected through hyperedge HE-3 (induced by source paper "intrinsically motivated autonomy in human robot interaction human perception of predictive information in robots"):
  with members:
    - "utilizing bluetooth low energy to recognize proximity touch and humans"

Connected through hyperedge HE-4 (induced by source paper "computational theories of curiosity driven learning"):
  with members:
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "policy search in continuous action domains an overview"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "hindsight experience replay"
    - "reverse curriculum generation for reinforcement learning"
    - "count based exploration with neural density models"
    - "the intentional unintentional agent learning to solve many continuous control tasks simultaneously"
    - "intrinsically motivated goal exploration processes with automatic curriculum learning"

Connected through hyperedge HE-5 (induced by source paper "expanding the active inference landscape more intrinsic motivations in the perception action loop"):
  with members:
    - "new and surprising ways to be mean adversarial npcs with coupled empowerment minimisation"
    - "accelerating empowerment computation with uct tree search"
    - "universal reinforcement learning algorithms survey and experiments"
    - "minimum information lqg control part ii retentive controllers"
    - "nonparametric general reinforcement learning"
    - "variational intrinsic control"
    - "the umwelt of an embodied agent a measure theoretic definition"
    - "generalized thompson sampling for sequential decision making and causal inference"

Connected through hyperedge HE-6 (induced by source paper "self organized control for musculoskeletal robots"):
  with members:
    - "novel plasticity rule can explain the development of sensorimotor intelligence"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"

Connected through hyperedge HE-7 (induced by source paper "quantifying emergent behavior of autonomous robots"):
  with members:
    - "novel plasticity rule can explain the development of sensorimotor intelligence"
    - "guiding designs of self organizing swarms interactive and automated approaches"
    - "empowerment an introduction"
    - "higher coordination with less control a result of information maximization in the sensorimotor loop"

Connected through hyperedge HE-8 (induced by source paper "universal memory architectures for autonomous machines"):
  with members:
    - "playing atari with deep reinforcement learning"
    - "metatheory of actions beyond consistency"

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.RO(Robotics)