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"
  Abstract: We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts. We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources. Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation. Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn. After that, we discuss RL applications, including games, robotics, natural language processing (NLP), computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, and, science, engineering, and art. Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue.

Connected through hyperedge HE-1 (induced by source paper "capsule network performance with autonomous navigation"):
  with members:
    - "capsule neural network based height classification using low cost automotive ultrasonic sensors"
    - "advanced capsule networks via context awareness"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "simultaneous localization and mapping with depth prediction using capsule networks for uavs"
    - "state representation learning with recurrent capsule networks"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "dynamic routing between capsules"

Connected through hyperedge HE-2 (induced by source paper "towards crisp ml q a machine learning process model with quality assurance methodology"):
  with members:
    - "quantifying interpretability and trust in machine learning systems"
    - "machine learning testing survey landscapes and horizons"
    - "deep visual domain adaptation a survey"
    - "on the state of the art of evaluation in neural language models"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "machine teaching a new paradigm for building machine learning systems"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "end to end vision based adaptive cruise control acc using deep reinforcement learning"):
  with members:
    - "end to end learning for self driving cars"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning in healthcare a survey"):
  with members:
    - "does the artificial intelligence clinician learn optimal treatment strategies for sepsis in intensive care"
    - "a dual mode adaptive basal bolus advisor based on reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "improving sepsis treatment strategies by combining deep and kernel based reinforcement learning"
    - "partial policy based reinforcement learning for anatomical landmark localization in 3d medical images"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "automatic view planning with multi scale deep reinforcement learning agents"
    - "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"

Connected through hyperedge HE-5 (induced by source paper "offworld gym open access physical robotics environment for real world reinforcement learning benchmark and research"):
  with members:
    - "replab a reproducible low cost arm benchmark platform for robotic learning"
    - "learning latent state representation for speeding up exploration"
    - "go explore a new approach for hard exploration problems"
    - "pyrep bringing v rep to deep robot learning"
    - "self supervised exploration via disagreement"
    - "provably efficient imitation learning from observation alone"
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "challenges of real world reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "challenges of real world reinforcement learning"):
  with members:
    - "value constrained model free continuous control"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "reward constrained policy optimization"

Connected through hyperedge HE-7 (induced by source paper "signal instructed coordination in cooperative multi agent reinforcement learning"):
  with members:
    - "on the pitfalls of measuring emergent communication"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "correlation in extensive form games saddle point formulation and benchmarks"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "federated learning for mobile keyboard prediction"
    - "progressive neural networks"
    - "neural slam learning to explore with external memory"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "a deep hierarchical approach to lifelong learning in minecraft"

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)