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: "deep reinforcement learning for robotic manipulation the state of the art"
  Abstract: The focus of this work is to enumerate the various approaches and algorithms that center around application of reinforcement learning in robotic ma- ]]nipulation tasks. Earlier methods utilized specialized policy representations and human demonstrations to constrict the policy. Such methods worked well with continuous state and policy space of robots but failed to come up with generalized policies. Subsequently, high dimensional non-linear function approximators like neural networks have been used to learn policies from scratch. Several novel and recent approaches have also embedded control policy with efficient perceptual representation using deep learning. This has led to the emergence of a new branch of dynamic robot control system called deep r inforcement learning(DRL). This work embodies a survey of the most recent algorithms, architectures and their implementations in simulations and real world robotic platforms. The gamut of DRL architectures are partitioned into two different branches namely, discrete action space algorithms(DAS) and continuous action space algorithms(CAS). Further, the CAS algorithms are divided into stochastic continuous action space(SCAS) and deterministic continuous action space(DCAS) algorithms. Along with elucidating an organ- isation of the DRL algorithms this work also manifests some of the state of the art applications of these approaches in robotic manipulation tasks.

Connected through hyperedge HE-1 (induced by source paper "accelerating reinforcement learning for reaching using continuous curriculum learning"):
  with members:
    - "harnessing reinforcement learning for neural motion planning"
    - "interactive open ended object affordance and grasp learning for robotic manipulation"
    - "accuracy based curriculum learning in deep reinforcement learning"
    - "deep learning using rectified linear units relu"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "hindsight experience replay"
    - "overcoming exploration in reinforcement learning with demonstrations"

Connected through hyperedge HE-2 (induced by source paper "lift the flap context reasoning using object centered graphs"):
  with members:
    - "learning scene gist with convolutional neural networks to improve object recognition"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "scene graph generation by iterative message passing"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "the more you know using knowledge graphs for image classification"
    - "interaction networks for learning about objects relations and physics"
    - "learning structured inference neural networks with label relations"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"

Connected through hyperedge HE-3 (induced by source paper "adaptive power system emergency control using deep reinforcement learning"):
  with members:
    - "learning navigation behaviors end to end with autorl"
    - "safe exploration in continuous action spaces"
    - "sim to real learning agile locomotion for quadruped robots"
    - "interpss a new generation power system simulation engine"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "constrained policy optimization"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-4 (induced by source paper "episodic memory deep q networks"):
  with members:
    - "learning to play in a day faster deep reinforcement learning by optimality tightening"
    - "asynchronous methods for deep reinforcement learning"
    - "prioritized experience replay"
    - "deep reinforcement learning with double q learning"
    - "the arcade learning environment an evaluation platform for general agents"

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)