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: "modular networks learning to decompose neural computation"
  Abstract: Scaling model capacity has been vital in the success of deep learning. For a typical network, necessary compute resources and training time grow dramatically with model size. Conditional computation is a promising way to increase the number of parameters with a relatively small increase in resources. We propose a training algorithm that flexibly chooses neural modules based on the data to be processed. Both the decomposition and modules are learned end-to-end. In contrast to existing approaches, training does not rely on regularization to enforce diversity in module use. We apply modular networks both to image recognition and language modeling tasks, where we achieve superior performance compared to several baselines. Introspection reveals that modules specialize in interpretable contexts.

Connected through hyperedge HE-1 (induced by source paper "towards modular algorithm induction"):
  with members:
    - "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"
    - "learning simple algorithms from examples"

Connected through hyperedge HE-2 (induced by source paper "recurrent independent mechanisms"):
  with members:
    - "learning powerful policies by using consistent dynamics model"
    - "routing networks and the challenges of modular and compositional computation"
    - "reinforcement learning with competitive ensembles of information constrained primitives"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "babyai first steps towards grounded language learning with a human in the loop"

Connected through hyperedge HE-3 (induced by source paper "routing networks and the challenges of modular and compositional computation"):
  with members:
    - "a meta mdp approach to exploration for lifelong reinforcement learning"
    - "reinforcement learning without backpropagation or a clock"
    - "the termination critic"
    - "learning abstract options"
    - "evolutionary architecture search for deep multitask networks"
    - "modular meta learning"
    - "diversity is all you need learning skills without a reward function"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"

Connected through hyperedge HE-4 (induced by source paper "structure learning for neural module networks"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "evolving deep neural networks"
    - "modulating early visual processing by language"
    - "inferring and executing programs for visual reasoning"

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)