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 intelligent transportation systems"
  Abstract: Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe. Rapidly advancing vehicular communication and edge cloud computation technologies provide key enablers for smart traffic management. However, operating viable real-time actuation mechanisms on a practically relevant scale involves formidable challenges, e.g., policy iteration and conventional Reinforcement Learning (RL) techniques suffer from poor scalability due to state space explosion. Motivated by these issues, we explore the potential for Deep Q-Networks (DQN) to optimize traffic light control policies. As an initial benchmark, we establish that the DQN algorithms yield the "thresholding" policy in a single-intersection. Next, we examine the scalability properties of DQN algorithms and their performance in a linear network topology with several intersections along a main artery. We demonstrate that DQN algorithms produce intelligent behavior, such as the emergence of "greenwave" patterns, reflecting their ability to learn favorable traffic light actuations.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for unmanned aerial vehicle assisted vehicular networks"):
  with members:
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "cellular enabled uav communication trajectory optimization under connectivity constraint"
    - "an online ride sharing path planning strategy for public vehicle systems"
    - "power control in uav supported ultra dense networks communications caching and energy transfer"
    - "joint transportation and charging scheduling in public vehicle systems a game theoretic approach"
    - "sgd learns the conjugate kernel class of the network"
    - "capacity of uav enabled multicast channel joint trajectory design and power allocation"
    - "neural optimizer search with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "spatial influence aware reinforcement learning for intelligent transportation system"):
  with members:
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "an online ride sharing path planning strategy for public vehicle systems"
    - "joint transportation and charging scheduling in public vehicle systems a game theoretic approach"
    - "exploiting social tie structure for cooperative wireless networking a social group utility maximization framework"
    - "continuous control with deep reinforcement learning"
    - "multiagent cooperation and competition with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "practical deep reinforcement learning approach for stock trading"):
  with members:
    - "large scale study of curiosity driven learning"
    - "continuous control with deep reinforcement learning"

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)