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: "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
  Abstract: Real-time strategy games have been an important field of game artificial intelligence in recent years. This paper presents a reinforcement learning and curriculum transfer learning method to control multiple units in StarCraft micromanagement. We define an efficient state representation, which breaks down the complexity caused by the large state space in the game environment. Then a parameter sharing multi-agent gradientdescent Sarsa({\lambda}) (PS-MAGDS) algorithm is proposed to train the units. The learning policy is shared among our units to encourage cooperative behaviors. We use a neural network as a function approximator to estimate the action-value function, and propose a reward function to help units balance their move and attack. In addition, a transfer learning method is used to extend our model to more difficult scenarios, which accelerates the training process and improves the learning performance. In small scale scenarios, our units successfully learn to combat and defeat the built-in AI with 100% win rates. In large scale scenarios, curriculum transfer learning method is used to progressively train a group of units, and shows superior performance over some baseline methods in target scenarios. With reinforcement learning and curriculum transfer learning, our units are able to learn appropriate strategies in StarCraft micromanagement scenarios.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "an optimization framework for task sequencing in curriculum learning"
    - "curriculum learning for cumulative return maximization"
    - "evolutionarily curated curriculum learning for deep reinforcement learning agents"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "learning curriculum policies for reinforcement learning"
    - "screenernet learning self paced curriculum for deep neural networks"
    - "theory of curriculum learning with convex loss functions"

Connected through hyperedge HE-2 (induced by source paper "the starcraft multi agent challenge"):
  with members:
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "value propagation networks"
    - "mean field multi agent reinforcement learning"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "on reinforcement learning for full length game of starcraft"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-3 (induced by source paper "hierarchical critics assignment for multi agent reinforcement learning"):
  with members:
    - "the representational capacity of action value networks for multi agent reinforcement learning"
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "data efficient hierarchical reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "unity a general platform for intelligent agents"
    - "meta learning shared hierarchies"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "hierarchical reinforcement learning for multi agent moba game"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "episodic memory deep q networks"
    - "self imitation learning"
    - "hierarchical macro strategy model for moba game ai"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "meta learning shared hierarchies"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "an optimization framework for task sequencing in curriculum learning"):
  with members:
    - "curriculum learning for cumulative return maximization"
    - "automatic goal generation for reinforcement learning agents"
    - "intrinsic motivation and automatic curricula via asymmetric self play"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"

Connected through hyperedge HE-6 (induced by source paper "curriculum learning for cumulative return maximization"):
  with members:
    - "an optimization framework for task sequencing in curriculum learning"
    - "automatic goal generation for reinforcement learning agents"
    - "progressive neural networks"
    - "intrinsic motivation and automatic curricula via asymmetric self play"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-7 (induced by source paper "modular architecture for starcraft ii with deep reinforcement learning"):
  with members:
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "counterfactual multi agent policy gradients"
    - "learning macromanagement in starcraft from replays using deep learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "a deep hierarchical approach to lifelong learning in minecraft"

Connected through hyperedge HE-8 (induced by source paper "macro action selection with deep reinforcement learning in starcraft"):
  with members:
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "distributed prioritized experience replay"
    - "revisiting the master slave architecture in multi agent deep reinforcement learning"
    - "torchcraft a library for machine learning research on real time strategy games"
    - "deep recurrent q learning for partially observable mdps"
    - "a dataset for starcraft ai an example of armies clustering"
    - "inferring strategies from limited reconnaissance in real time strategy games"

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.AI(Artificial Intelligence)