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: "counter factual reinforcement learning how to model decision makers that anticipate the future"
  Abstract: This paper introduces a novel framework for modeling interacting humans in a multi-stage game. This "iterated semi network-form game" framework has the following desirable characteristics: (1) Bounded rational players, (2) strategic players (i.e., players account for one another's reward functions when predicting one another's behavior), and (3) computational tractability even on real-world systems. We achieve these benefits by combining concepts from game theory and reinforcement learning. To be precise, we extend the bounded rational "level-K reasoning" model to apply to games over multiple stages. Our extension allows the decomposition of the overall modeling problem into a series of smaller ones, each of which can be solved by standard reinforcement learning algorithms. We call this hybrid approach "level-K reinforcement learning". We investigate these ideas in a cyber battle scenario over a smart power grid and discuss the relationship between the behavior predicted by our model and what one might expect of real human defenders and attackers.

Connected through hyperedge HE-1 (induced by source paper "modeling cyber physical human systems via an interplay between reinforcement learning and game theory"):
  with members:
    - "a game theoretical framework for the evaluation of unmanned aircraft systems airspace integration concepts"
    - "reachability based safety and goal satisfaction of unmanned aerial platoons on air highways"
    - "provably safe and robust drone routing via sequential path planning a case study in san francisco and the bay area"
    - "game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems"
    - "safe platooning of unmanned aerial vehicles via reachability"
    - "cyber physical security a game theory model of humans interacting over control systems"
    - "game theoretic modeling of pilot behavior during mid air encounters"

Connected through hyperedge HE-2 (induced by source paper "the power of d choices in scheduling for data centers with heterogeneous servers"):
  with members:
    - "a game theoretical framework for the evaluation of unmanned aircraft systems airspace integration concepts"
    - "blind gb pandas a blind throughput optimal load balancing algorithm for affinity scheduling"
    - "gb pandas throughput and heavy traffic optimality analysis for affinity scheduling"
    - "near data scheduling for data centers with multiple levels of data locality"
    - "a mobile geo communication dataset for physiology aware dash in rural ambulance transport"
    - "affinity scheduling and the applications on data center scheduling with data locality"

Connected through hyperedge HE-3 (induced by source paper "a game theoretical framework for the evaluation of unmanned aircraft systems airspace integration concepts"):
  with members:
    - "game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems"
    - "cyber physical security a game theory model of humans interacting over control systems"
    - "game theoretic modeling of pilot behavior during mid air encounters"

Connected through hyperedge HE-4 (induced by source paper "cyber physical security a game theory model of humans interacting over control systems"):
  with members:
    - "game theoretic modeling of pilot behavior during mid air encounters"
    - "stochastic games for security in networks with interdependent nodes"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "value of information in noncooperative games"):
  with members:
    - "collective intelligence data routing and braess paradox"

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)