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 simple algorithms from examples"
  Abstract: We present an approach for learning simple algorithms such as copying, multi-digit addition and single digit multiplication directly from examples. Our framework consists of a set of interfaces, accessed by a controller. Typical interfaces are 1-D tapes or 2-D grids that hold the input and output data. For the controller, we explore a range of neural network-based models which vary in their ability to abstract the underlying algorithm from training instances and generalize to test examples with many thousands of digits. The controller is trained using $Q$-learning with several enhancements and we show that the bottleneck is in the capabilities of the controller rather than in the search incurred by $Q$-learning.

Connected through hyperedge HE-1 (induced by source paper "towards modular algorithm induction"):
  with members:
    - "modular networks learning to decompose neural computation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "automatically composing representation transformations as a means for generalization"
    - "robustfill neural program learning under noisy i o"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "deepcoder learning to write programs"
    - "neural random access machines"

Connected through hyperedge HE-2 (induced by source paper "learning implicitly recurrent cnns through parameter sharing"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "regularized evolution for image classifier architecture search"
    - "neural arithmetic logic units"
    - "snas stochastic neural architecture search"
    - "sparsely aggregated convolutional networks"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "making neural programming architectures generalize via recursion"

Connected through hyperedge HE-3 (induced by source paper "neural shuffle exchange networks sequence processing in o n log n time"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "star transformer"
    - "universal transformers"
    - "generating long sequences with sparse transformers"
    - "tensor2tensor for neural machine translation"
    - "recent advances in neural program synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"

Connected through hyperedge HE-4 (induced by source paper "neural shape parsers for constructive solid geometry"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "learning to infer and execute 3d shape programs"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "inferring and executing programs for visual reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "csgnet neural shape parser for constructive solid geometry"

Connected through hyperedge HE-5 (induced by source paper "memory augmented recurrent neural networks can learn generalized dyck languages"):
  with members:
    - "theoretical limitations of self attention in neural sequence models"
    - "sequential neural networks as automata"
    - "context free transductions with neural stacks"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "on evaluating the generalization of lstm models in formal languages"
    - "attention is all you need"
    - "lie access neural turing machine"
    - "learning operations on a stack with neural turing machines"

Connected through hyperedge HE-6 (induced by source paper "model predictive policy learning with uncertainty regularization for driving in dense traffic"):
  with members:
    - "stochastic video generation with a learned prior"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "stochastic variational video prediction"
    - "learning model based planning from scratch"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "imagination augmented agents for deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "autocorrect deep inductive alignment of noisy geometric annotations"):
  with members:
    - "smooth loss functions for deep top k classification"
    - "inductive visual localisation factorised training for superior generalisation"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "cycada cycle consistent adversarial domain adaptation"
    - "making neural programming architectures generalize via recursion"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning from noisy large scale datasets with minimal supervision"
    - "spatial memory for context reasoning in object detection"

Connected through hyperedge HE-8 (induced by source paper "learning algorithmic solutions to symbolic planning tasks with a neural computer"):
  with members:
    - "feed forward neural networks need inductive bias to learn equality relations"
    - "unsupervised predictive memory in a goal directed agent"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "neural programmer inducing latent programs with gradient descent"
    - "inferring algorithmic patterns with stack augmented recurrent nets"
    - "neural gpus learn algorithms"
    - "ask me anything dynamic memory networks for natural language processing"
    - "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)