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: "mazebase a sandbox for learning from games"
  Abstract: This paper introduces MazeBase: an environment for simple 2D games, designed as a sandbox for machine learning approaches to reasoning and planning. Within it, we create 10 simple games embodying a range of algorithmic tasks (e.g. if-then statements or set negation). A variety of neural models (fully connected, convolutional network, memory network) are deployed via reinforcement learning on these games, with and without a procedurally generated curriculum. Despite the tasks' simplicity, the performance of the models is far from optimal, suggesting directions for future development. We also demonstrate the versatility of MazeBase by using it to emulate small combat scenarios from StarCraft. Models trained on the MazeBase version can be directly applied to StarCraft, where they consistently beat the in-game AI.

Connected through hyperedge HE-1 (induced by source paper "why build an assistant in minecraft"):
  with members:
    - "craftassist a framework for dialogue enabled interactive agents"
    - "habitat a platform for embodied ai research"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "towards deep conversational recommendations"
    - "learning to ask questions in open domain conversational systems with typed decoders"

Connected through hyperedge HE-2 (induced by source paper "audio visual embodied navigation"):
  with members:
    - "two body problem collaborative visual task completion"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "do autonomous agents benefit from hearing"
    - "the replica dataset a digital replica of indoor spaces"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "co separating sounds of visual objects"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "interactive gibson benchmark for interactive navigation in cluttered environments"

Connected through hyperedge HE-3 (induced by source paper "two body problem collaborative visual task completion"):
  with members:
    - "tarmac targeted multi agent communication"
    - "understanding grounded language learning agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "neural modular control for embodied question answering"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "unifying map and landmark based representations for visual navigation"
    - "home a household multimodal environment"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "hierarchical decision making by generating and following natural language instructions"):
  with members:
    - "relational deep reinforcement learning"
    - "speaker follower models for vision and language navigation"
    - "hierarchical macro strategy model for moba game ai"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "unified pragmatic models for generating and following instructions"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "starcraft ii a new challenge for reinforcement learning"
    - "parlai a dialog research software platform"

Connected through hyperedge HE-5 (induced by source paper "continual and multi task reinforcement learning with shared episodic memory"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "meta learning shared hierarchies"
    - "efficient parallel methods for deep reinforcement learning"
    - "gated attention architectures for task oriented language grounding"
    - "neural map structured memory for deep reinforcement learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "neural episodic control"
    - "mapping instructions and visual observations to actions with reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "qualitative numeric planning reductions and complexity"):
  with members:
    - "transfer of deep reactive policies for mdp planning"
    - "compact policies for fully observable non deterministic planning as sat"
    - "features projections and representation change for generalized planning"
    - "learning features and abstract actions for computing generalized plans"
    - "learning generalized reactive policies using deep neural networks"
    - "action schema networks generalised policies with deep learning"
    - "first order decision diagrams for relational mdps"
    - "the computational complexity of probabilistic planning"

Connected through hyperedge HE-7 (induced by source paper "babyai a platform to study the sample efficiency of grounded language learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "learning to understand goal specifications by modelling reward"
    - "gated attention architectures for task oriented language grounding"
    - "deep tamer interactive agent shaping in high dimensional state spaces"
    - "film visual reasoning with a general conditioning layer"
    - "grounded language learning in a simulated 3d world"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-8 (induced by source paper "value propagation networks"):
  with members:
    - "the bottleneck simulator a model based deep reinforcement learning approach"
    - "cooperative motion planning for non holonomic agents with value iteration networks"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "learning generalized reactive policies using deep neural networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "value prediction network"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "generalized value iteration networks life beyond lattices"

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.LG(Machine Learning)