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: "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
  Abstract: To operate effectively in tomorrow's smart cities, autonomous vehicles (AVs) must rely on intra-vehicle sensors such as camera and radar as well as inter-vehicle communication. Such dependence on sensors and communication links exposes AVs to cyber-physical (CP) attacks by adversaries that seek to take control of the AVs by manipulating their data. Thus, to ensure safe and optimal AV dynamics control, the data processing functions at AVs must be robust to such CP attacks. To this end, in this paper, the state estimation process for monitoring AV dynamics, in presence of CP attacks, is analyzed and a novel adversarial deep reinforcement learning (RL) algorithm is proposed to maximize the robustness of AV dynamics control to CP attacks. The attacker's action and the AV's reaction to CP attacks are studied in a game-theoretic framework. In the formulated game, the attacker seeks to inject faulty data to AV sensor readings so as to manipulate the inter-vehicle optimal safe spacing and potentially increase the risk of AV accidents or reduce the vehicle flow on the roads. Meanwhile, the AV, acting as a defender, seeks to minimize the deviations of spacing so as to ensure robustness to the attacker's actions. Since the AV has no information about the attacker's action and due to the infinite possibilities for data value manipulations, the outcome of the players' past interactions are fed to long-short term memory (LSTM) blocks. Each player's LSTM block learns the expected spacing deviation resulting from its own action and feeds it to its RL algorithm. Then, the the attacker's RL algorithm chooses the action which maximizes the spacing deviation, while the AV's RL algorithm tries to find the optimal action that minimizes such deviation.

Connected through hyperedge HE-1 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles"):
  with members:
    - "reinforcement learning with probabilistic guarantees for autonomous driving"
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "lane change decision making through deep reinforcement learning with rule based constraints"
    - "safe efficient and comfortable velocity control based on reinforcement learning for autonomous driving"
    - "autonomous highway driving using deep reinforcement learning"
    - "improved robustness and safety for autonomous vehicle control with adversarial reinforcement learning"
    - "human like autonomous car following model with deep reinforcement learning"
    - "combining planning and deep reinforcement learning in tactical decision making for autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for cyber security"):
  with members:
    - "deep reinforcement learning for detecting malicious websites"
    - "manipulating soft tissues by deep reinforcement learning for autonomous robotic surgery"
    - "multi agent deep reinforcement learning with human strategies"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "relational deep reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"

Connected through hyperedge HE-3 (induced by source paper "online robustness training for deep reinforcement learning"):
  with members:
    - "certified adversarial robustness for deep reinforcement learning"
    - "a provable defense for deep residual networks"
    - "scaling provable adversarial defenses"
    - "adversary a3c for robust reinforcement learning"
    - "training verified learners with learned verifiers"
    - "online robust policy learning in the presence of unknown adversaries"
    - "certified defenses against adversarial examples"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "experienced deep reinforcement learning with generative adversarial networks gans for model free ultra reliable low latency communication"):
  with members:
    - "generative adversarial networks for distributed intrusion detection in the internet of things"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "joint user association and resource allocation optimization for ultra reliable low latency hetnets"
    - "federated learning for ultra reliable low latency v2v communications"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning with feedback based exploration"):
  with members:
    - "learning gaussian policies from corrective human feedback"
    - "interactive learning with corrective feedback for policies based on deep neural networks"
    - "including uncertainty when learning from human corrections"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "a distributional perspective on reinforcement learning"
    - "overcoming exploration in reinforcement learning with demonstrations"

Connected through hyperedge HE-6 (induced by source paper "drones in distress a game theoretic countermeasure for protecting uavs against gps spoofing"):
  with members:
    - "dynamic mobility aware interference avoidance for aerial base stations in cognitive radio networks"
    - "a game of drones cyber physical security of time critical uav applications with cumulative prospect theory perceptions and valuations"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "machine learning for wireless connectivity and security of cellular connected uavs"
    - "cyber physical security and safety of autonomous connected vehicles optimal control meets multi armed bandit learning"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "single controller stochastic games for optimized moving target defense"

Connected through hyperedge HE-7 (induced by source paper "local voting games for misbehavior detection in vanets in presence of uncertainty"):
  with members:
    - "risk assessment of autonomous vehicles using bayesian defense graphs"
    - "survey on misbehavior detection in cooperative intelligent transportation systems"
    - "error correction coding meets cyber physical systems message passing analysis of self healing interdependent networks"

Connected through hyperedge HE-8 (induced by source paper "optimal time allocation in vanets advertising a price based approach using stackelberg game"):
  with members:
    - "dynamic mobility aware interference avoidance for aerial base stations in cognitive radio networks"
    - "deep learning for reliable mobile edge analytics in intelligent transportation systems"

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.SY(Systems and Control)