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: "routing networks and the challenges of modular and compositional computation"
  Abstract: Compositionality is a key strategy for addressing combinatorial complexity and the curse of dimensionality. Recent work has shown that compositional solutions can be learned and offer substantial gains across a variety of domains, including multi-task learning, language modeling, visual question answering, machine comprehension, and others. However, such models present unique challenges during training when both the module parameters and their composition must be learned jointly. In this paper, we identify several of these issues and analyze their underlying causes. Our discussion focuses on routing networks, a general approach to this problem, and examines empirically the interplay of these challenges and a variety of design decisions. In particular, we consider the effect of how the algorithm decides on module composition, how the algorithm updates the modules, and if the algorithm uses regularization.

Connected through hyperedge HE-1 (induced by source paper "gradient surgery for multi task learning"):
  with members:
    - "distilling policy distillation"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "branched multi task networks deciding what layers to share"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "attentive single tasking of multiple tasks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-2 (induced by source paper "recurrent independent mechanisms"):
  with members:
    - "learning powerful policies by using consistent dynamics model"
    - "reinforcement learning with competitive ensembles of information constrained primitives"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "modular networks learning to decompose neural computation"
    - "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 "reinforcement learning with competitive ensembles of information constrained primitives"):
  with members:
    - "model primitive hierarchical lifelong reinforcement learning"
    - "hierarchical visuomotor control of humanoids"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "neural probabilistic motor primitives for humanoid control"
    - "inferring and executing programs for visual reasoning"
    - "meta learning shared hierarchies"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-4 (induced by source paper "on the role of weight sharing during deep option learning"):
  with members:
    - "reinforcement learning without backpropagation or a clock"
    - "learning abstract options"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "progressive neural networks"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "scalable recollections for continual lifelong learning"

Connected through hyperedge HE-5 (induced by source paper "gumbel matrix routing for flexible multi task learning"):
  with members:
    - "group normalization"
    - "evolutionary architecture search for deep multitask networks"
    - "spottune transfer learning through adaptive fine tuning"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "convolutional networks with adaptive inference graphs"
    - "pathnet evolution channels gradient descent in super neural networks"

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)