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: "empirically evaluating multiagent learning algorithms"
  Abstract: There exist many algorithms for learning how to play repeated bimatrix games. Most of these algorithms are justified in terms of some sort of theoretical guarantee. On t he other hand, little is known about the empirical performance of these algorithms. Most such claims in the literature are been based on small experiments, which has hampered understanding as well as the development of new multiagent learning (MAL) algorithms. We have developed a new suite of tools for running multiagent experiments: the MultiAgent Learning Testbed (MALT). These tools are designed to facilitate larger and more comprehensive experiments by removing the need to build one-off experimental code. MALT also provides baseline implementations of many MAL algorithms, hopefully eliminating or reducing differences between algorithm implementations and increasing the reproducibility of results. Using this test suite, we ran an experiment unprecedented in size. We analyzed the results according to a variety of performance metrics including reward, maxmin distance, regret, and several notions of equilibrium conve rgence. We confirmed several pieces of conventional wisdom, but also discovered some surprising results. For example, we found that single-agent Q-learning outperformed many more complicated and more modern MAL algorithms.

Connected through hyperedge HE-1 (induced by source paper "stmarl a spatio temporal multi agent reinforcement learning approach for cooperative traffic light control"):
  with members:
    - "cityflow a multi agent reinforcement learning environment for large scale city traffic scenario"
    - "colight learning network level cooperation for traffic signal control"
    - "relational deep reinforcement learning"
    - "relational inductive biases deep learning and graph networks"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "attention is all you need"
    - "neural message passing for quantum chemistry"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-2 (induced by source paper "learning multiagent coordination in the absence of communication channels"):
  with members:
    - "reinforcement learning produces dominant strategies for the iterated prisoner s dilemma"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning multiagent communication with backpropagation"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "learning implicit communication strategies for the purpose of illicit collusion"):
  with members:
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "learning with opponent learning awareness"):
  with members:
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "machine theory of mind"
    - "counterfactual multi agent policy gradients"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"

Connected through hyperedge HE-5 (induced by source paper "multi agent reinforcement learning in sequential social dilemmas"):
  with members:
    - "value function approximation in zero sum markov games"
    - "a framework for sequential planning in multi agent settings"

Connected through hyperedge HE-6 (induced by source paper "goal conflict in designing an autonomous artificial system"):
  with members:
    - "multi agent reinforcement learning in sequential social dilemmas"

Connected through hyperedge HE-7 (induced by source paper "learning to communicate to solve riddles with deep distributed recurrent q networks"):
  with members:
    - "prioritized experience replay"
    - "data efficient learning of feedback policies from image pixels using deep dynamical models"
    - "recurrent reinforcement learning a hybrid approach"
    - "increasing the action gap new operators for reinforcement learning"
    - "language understanding for text based games using deep reinforcement learning"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "action conditional video prediction using deep networks in atari games"
    - "dueling network architectures for 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.GT(Computer Science and Game Theory)