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: "recurrent independent mechanisms"
  Abstract: Learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes which only affect a few of the underlying causes. We propose Recurrent Independent Mechanisms (RIMs), a new recurrent architecture in which multiple groups of recurrent cells operate with nearly independent transition dynamics, communicate only sparingly through the bottleneck of attention, and are only updated at time steps where they are most relevant. We show that this leads to specialization amongst the RIMs, which in turn allows for dramatically improved generalization on tasks where some factors of variation differ systematically between training and evaluation.

Connected through hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"

Connected through hyperedge HE-2 (induced by source paper "graph neural networks meet neural symbolic computing a survey and perspective"):
  with members:
    - "the next decade in ai four steps towards robust artificial intelligence"
    - "run csp unsupervised learning of message passing networks for binary constraint satisfaction problems"
    - "deep learning for symbolic mathematics"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"
    - "the use of deep learning for symbolic integration a review of lample and charton 2019"
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "product kanerva machines factorized bayesian memory"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "monet unsupervised scene decomposition and representation"
    - "generalization of reinforcement learners with working and episodic memory"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "metalearned neural memory"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "shaping belief states with generative environment models for rl"
    - "optimizing agent behavior over long time scales by transporting value"

Connected through hyperedge HE-4 (induced by source paper "causality for machine learning"):
  with members:
    - "optimal decision making under strategic behavior"
    - "learning robust representations by projecting superficial statistics out"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "learning first order symbolic representations for planning from the structure of the state space"
    - "causal reasoning from meta reinforcement learning"
    - "competitive training of mixtures of independent deep generative models"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "domain generalization via conditional invariant representation"

Connected through hyperedge HE-5 (induced by source paper "sketchtransfer a challenging new task for exploring detail invariance and the abstractions learned by deep networks"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "kuronet pre modern japanese kuzushiji character recognition with deep learning"
    - "virtual mixup training for unsupervised domain adaptation"
    - "graphmix regularized training of graph neural networks for semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "high frequency component helps explain the generalization of convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "working memory graphs"):
  with members:
    - "metalearned neural memory"
    - "relational recurrent neural networks"
    - "attention is all you need"
    - "film visual reasoning with a general conditioning layer"
    - "asynchronous methods for deep reinforcement learning"
    - "control of memory active perception and action in minecraft"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "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.LG(Machine Learning)