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: "learning macromanagement in starcraft from replays using deep learning"
  Abstract: The real-time strategy game StarCraft has proven to be a challenging environment for artificial intelligence techniques, and as a result, current state-of-the-art solutions consist of numerous hand-crafted modules. In this paper, we show how macromanagement decisions in StarCraft can be learned directly from game replays using deep learning. Neural networks are trained on 789,571 state-action pairs extracted from 2,005 replays of highly skilled players, achieving top-1 and top-3 error rates of 54.6% and 22.9% in predicting the next build action. By integrating the trained network into UAlbertaBot, an open source StarCraft bot, the system can significantly outperform the game's built-in Terran bot, and play competitively against UAlbertaBot with a fixed rush strategy. To our knowledge, this is the first time macromanagement tasks are learned directly from replays in StarCraft. While the best hand-crafted strategies are still the state-of-the-art, the deep network approach is able to express a wide range of different strategies and thus improving the network's performance further with deep reinforcement learning is an immediately promising avenue for future research. Ultimately this approach could lead to strong StarCraft bots that are less reliant on hard-coded strategies.

Connected through hyperedge HE-1 (induced by source paper "defoggan predicting hidden information in the starcraft fog of war with generative adversarial nets"):
  with members:
    - "single image haze removal using conditional wasserstein generative adversarial networks"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "macro action selection with deep reinforcement learning in starcraft"
    - "forward modeling for partial observation strategy games a starcraft defogger"
    - "clear the fog combat value assessment in incomplete information games with convolutional encoder decoders"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "improved training of wasserstein gans"
    - "stardata a starcraft ai research dataset"

Connected through hyperedge HE-2 (induced by source paper "evolutionary reinforcement learning for sample efficient multiagent coordination"):
  with members:
    - "collaborative evolutionary reinforcement learning"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "emergent coordination through competition"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "pommerman a multi agent playground"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-3 (induced by source paper "learning a behavioral repertoire from demonstrations"):
  with members:
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "diversity is all you need learning skills without a reward function"
    - "imitation learning with concurrent actions in 3d games"
    - "msc a dataset for macro management in starcraft ii"
    - "hindsight experience replay"
    - "teacher student curriculum learning"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "deep learning for video game playing"

Connected through hyperedge HE-4 (induced by source paper "on reinforcement learning for full length game of starcraft"):
  with members:
    - "relational deep reinforcement learning"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "meta learning shared hierarchies"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "feudal networks for hierarchical reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "proximal policy optimization algorithms"
    - "the option critic architecture"

Connected through hyperedge HE-5 (induced by source paper "forward modeling for partial observation strategy games a starcraft defogger"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "stardata a starcraft ai research dataset"
    - "torchcraft a library for machine learning research on real time strategy games"
    - "episodic exploration for deep deterministic policies an application to starcraft micromanagement tasks"
    - "learning physical intuition of block towers by example"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-6 (induced by source paper "modular architecture for starcraft ii with deep reinforcement learning"):
  with members:
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "counterfactual multi agent policy gradients"
    - "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-7 (induced by source paper "asynchronous advantage actor critic agent for starcraft ii"):
  with members:
    - "starcraft ii a new challenge for reinforcement learning"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "sim to real robot learning from pixels with progressive nets"

Connected through hyperedge HE-8 (induced by source paper "high level strategy selection under partial observability in starcraft brood war"):
  with members:
    - "forward modeling for partial observation strategy games a starcraft defogger"
    - "starcraft ii a new challenge for reinforcement learning"
    - "adam a method for stochastic optimization"

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)