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: "measuring abstract reasoning in neural networks"
  Abstract: Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate. Here, we propose a dataset and challenge designed to probe abstract reasoning, inspired by a well-known human IQ test. To succeed at this challenge, models must cope with various generalisation `regimes' in which the training and test data differ in clearly-defined ways. We show that popular models such as ResNets perform poorly, even when the training and test sets differ only minimally, and we present a novel architecture, with a structure designed to encourage reasoning, that does significantly better. When we vary the way in which the test questions and training data differ, we find that our model is notably proficient at certain forms of generalisation, but notably weak at others. We further show that the model's ability to generalise improves markedly if it is trained to predict symbolic explanations for its answers. Altogether, we introduce and explore ways to both measure and induce stronger abstract reasoning in neural networks. Our freely-available dataset should motivate further progress in this direction.

Connected through hyperedge HE-1 (induced by source paper "making sense of sensory input"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "model based reinforcement learning for atari"
    - "unsupervised discovery of parts structure and dynamics"
    - "composable planning with attributes"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "graph networks as learnable physics engines for inference and control"
    - "semi supervised online structure learning for composite event recognition"
    - "flexible neural representation for physics prediction"

Connected through hyperedge HE-2 (induced by source paper "modeling gestalt visual reasoning on the raven s progressive matrices intelligence test using generative image inpainting techniques"):
  with members:
    - "learning to make analogies by contrasting abstract relational structure"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "raven a dataset for relational and analogical visual reasoning"
    - "pluralistic image completion"
    - "improving generalization for abstract reasoning tasks using disentangled feature representations"
    - "generative image inpainting with contextual attention"
    - "generative face completion"
    - "deep image prior"

Connected through hyperedge HE-3 (induced by source paper "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"):
  with members:
    - "synaptic plasticity dynamics for deep continuous local learning decolle"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "lstm a search space odyssey"
    - "understanding synthetic gradients and decoupled neural interfaces"
    - "deep rewiring training very sparse deep networks"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "learning to reinforcement learn"

Connected through hyperedge HE-4 (induced by source paper "neural theorem provers do not learn rules without exploration"):
  with members:
    - "neural logic machines"
    - "a comprehensive survey on graph neural networks"
    - "deepproblog neural probabilistic logic programming"
    - "logical rule induction and theory learning using neural theorem proving"
    - "towards neural theorem proving at scale"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "end to end differentiable proving"

Connected through hyperedge HE-5 (induced by source paper "training neural networks to encode symbols enables combinatorial generalization"):
  with members:
    - "learning to make analogies by contrasting abstract relational structure"
    - "colorless green recurrent networks dream hierarchically"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a neural algorithm of artistic style"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "do neural networks show gestalt phenomena an exploration of the law of closure"):
  with members:
    - "do better imagenet models transfer better"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "network dissection quantifying interpretability of deep visual representations"
    - "deep image prior"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-7 (induced by source paper "modelling identity rules with neural networks"):
  with members:
    - "extrapolation in nlp"
    - "learning inductive biases with simple neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning a critical appraisal"
    - "dynamic routing between capsules"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "inductive bias of deep convolutional networks through pooling geometry"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "multi layer relation networks"):
  with members:
    - "working memory networks augmenting memory networks with a relational reasoning module"
    - "finding remo related memory object a simple neural architecture for text based reasoning"
    - "adaptive memory networks"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "relnet end to end modeling of entities relations"
    - "towards ai complete question answering a set of prerequisite toy tasks"

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)