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: "memory augmented control networks"
  Abstract: Planning problems in partially observable environments cannot be solved directly with convolutional networks and require some form of memory. But, even memory networks with sophisticated addressing schemes are unable to learn intelligent reasoning satisfactorily due to the complexity of simultaneously learning to access memory and plan. To mitigate these challenges we introduce the Memory Augmented Control Network (MACN). The proposed network architecture consists of three main parts. The first part uses convolutions to extract features and the second part uses a neural network-based planning module to pre-plan in the environment. The third part uses a network controller that learns to store those specific instances of past information that are necessary for planning. The performance of the network is evaluated in discrete grid world environments for path planning in the presence of simple and complex obstacles. We show that our network learns to plan and can generalize to new environments.

Connected through hyperedge HE-1 (induced by source paper "sufficiently accurate model learning"):
  with members:
    - "learning optimal resource allocations in wireless systems"
    - "deepmind control suite"
    - "visual reinforcement learning with imagined goals"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "gp ilqg data driven robust optimal control for uncertain nonlinear dynamical systems"
    - "constrained policy optimization"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-2 (induced by source paper "learning navigation costs from demonstration in partially observable environments"):
  with members:
    - "differentiable algorithm networks for composable robot learning"
    - "path integral networks end to end differentiable optimal control"
    - "qmdp net deep learning for planning under partial observability"
    - "reinforcement learning via recurrent convolutional neural networks"
    - "cognitive mapping and planning for visual navigation"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "value iteration networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "visual hide and seek"):
  with members:
    - "two body problem collaborative visual task completion"
    - "emergent tool use from multi agent autocurricula"
    - "emergent coordination through competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "habitat a platform for embodied ai research"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-4 (induced by source paper "benchmarking classic and learned navigation in complex 3d environments"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "group normalization"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "building generalizable agents with a realistic and rich 3d environment"

Connected through hyperedge HE-5 (induced by source paper "revisiting embodiedqa a simple baseline and beyond"):
  with members:
    - "triplet based deep hashing network for cross modal retrieval"
    - "multi target embodied question answering"
    - "active transfer learning network a unified deep joint spectral spatial feature learning model for hyperspectral image classification"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "blindfold baselines for embodied qa"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "neural modular control for embodied question answering"
    - "a better way to attend attention with trees for video question answering"

Connected through hyperedge HE-6 (induced by source paper "scene memory transformer for embodied agents in long horizon tasks"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "active neural localization"
    - "learning longer term dependencies in rnns with auxiliary losses"

Connected through hyperedge HE-7 (induced by source paper "learning to map natural language instructions to physical quadcopter control using simulated flight"):
  with members:
    - "stay on the path instruction fidelity in vision and language navigation"
    - "executing instructions in situated collaborative interactions"
    - "chasing ghosts instruction following as bayesian state tracking"
    - "universal planning networks"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "speaker follower models for vision and language navigation"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "mapping instructions to actions in 3d environments with visual goal prediction"

Connected through hyperedge HE-8 (induced by source paper "metalearned neural memory"):
  with members:
    - "contextual memory trees"
    - "learning attractor dynamics for generative memory"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "learning to reason with third order tensor products"
    - "metalearning with hebbian fast weights"
    - "one shot visual imitation learning via meta learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "rapid adaptation with conditionally shifted neurons"

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)