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 green security games with real time information"
  Abstract: Green Security Games (GSGs) have been proposed and applied to optimize patrols conducted by law enforcement agencies in green security domains such as combating poaching, illegal logging and overfishing. However, real-time information such as footprints and agents' subsequent actions upon receiving the information, e.g., rangers following the footprints to chase the poacher, have been neglected in previous work. To fill the gap, we first propose a new game model GSG-I which augments GSGs with sequential movement and the vital element of real-time information. Second, we design a novel deep reinforcement learning-based algorithm, DeDOL, to compute a patrolling strategy that adapts to the real-time information against a best-responding attacker. DeDOL is built upon the double oracle framework and the policy-space response oracle, solving a restricted game and iteratively adding best response strategies to it through training deep Q-networks. Exploring the game structure, DeDOL uses domain-specific heuristic strategies as initial strategies and constructs several local modes for efficient and parallelized training. To our knowledge, this is the first attempt to use Deep Q-Learning for security games.

Connected through hyperedge HE-1 (induced by source paper "mipaal mixed integer program as a layer"):
  with members:
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "decision focused learning of adversary behavior in security games"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "optnet differentiable optimization as a layer in neural networks"
    - "learning combinatorial optimization algorithms over graphs"
    - "position indexed formulations for kidney exchange"

Connected through hyperedge HE-2 (induced by source paper "bi level actor critic for multi agent coordination"):
  with members:
    - "a regularized opponent model with maximum entropy objective"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "mean field multi agent reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "counterfactual multi agent policy gradients"
    - "reinforcement learning with deep energy based policies"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "learning to communicate with deep multi agent reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "solving online threat screening games using constrained action space reinforcement learning"):
  with members:
    - "resource constrained deep reinforcement learning"
    - "robust stackelberg equilibria in extensive form games and extension to limited lookahead"
    - "optlayer practical constrained optimization for deep reinforcement learning in the real world"
    - "optnet differentiable optimization as a layer in neural networks"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "a generic metaheuristic approach to sequential security games"):
  with members:
    - "addressing expensive multi objective games with postponed preference articulation via memetic co evolution"

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.MA(Multiagent Systems)