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: "exploring applications of deep reinforcement learning for real world autonomous driving systems"
  Abstract: Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye's path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthetic car simulator environments such as TORCS and CARLA. In general, DRL is still at its infancy in terms of usability in real-world applications. Our goal in this paper is to encourage real-world deployment of DRL in various autonomous driving (AD) applications. We first provide an overview of the tasks in autonomous driving systems, reinforcement learning algorithms and applications of DRL to AD systems. We then discuss the challenges which must be addressed to enable further progress towards real-world deployment.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "multi objective multi agent decision making a utility based analysis and survey"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "on first order meta learning algorithms"
    - "a tempt to unify heterogeneous driving databases using traffic primitives"

Connected through hyperedge HE-2 (induced by source paper "social navigation with human empowerment driven reinforcement learning"):
  with members:
    - "deep local trajectory replanning and control for robot navigation"
    - "a survey on intrinsic motivation in reinforcement learning"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "emi exploration with mutual information"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "socially aware motion planning with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "learning to switch between machines and humans"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "regression under human assistance"
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "learner aware teaching inverse reinforcement learning with preferences and constraints"
    - "selectivenet a deep neural network with an integrated reject option"
    - "the algorithmic automation problem prediction triage and human effort"
    - "direct uncertainty prediction for medical second opinions"
    - "teaching inverse reinforcement learners via features and demonstrations"

Connected through hyperedge HE-4 (induced by source paper "a utility based analysis of equilibria in multi objective normal form games"):
  with members:
    - "multi objective multi agent decision making a utility based analysis and survey"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "ordered preference elicitation strategies for supporting multi objective decision making"
    - "multi objective deep reinforcement learning"
    - "structure in the value function of two player zero sum games of incomplete information"
    - "a survey of multi objective sequential decision making"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"
    - "reinforcement learning based resource allocation in fog radio access networks for various iot environments"

Connected through hyperedge HE-6 (induced by source paper "neurall towards a unified model for visual perception in automated driving"):
  with members:
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "yolov3 an incremental improvement"
    - "taskonomy disentangling task transfer learning"
    - "auxiliary tasks in multi task learning"
    - "learning efficient convolutional networks through network slimming"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "fast scene understanding for autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "variance reduction in actor critic methods acm"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "distributed prioritized experience replay"
    - "learning transferable architectures for scalable image recognition"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-8 (induced by source paper "on the computation of counterfactual explanations a survey"):
  with members:
    - "efficient computation of counterfactual explanations of lvq models"
    - "deep learning for audio signal processing"
    - "face feasible and actionable counterfactual explanations"
    - "a survey of the usages of deep learning in natural language processing"
    - "explaining explanations an overview of interpretability of machine learning"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "why should i trust you explaining the predictions of any classifier"

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)