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 to make analogies by contrasting abstract relational structure"
  Abstract: Analogical reasoning has been a principal focus of various waves of AI research. Analogy is particularly challenging for machines because it requires relational structures to be represented such that they can be flexibly applied across diverse domains of experience. Here, we study how analogical reasoning can be induced in neural networks that learn to perceive and reason about raw visual data. We find that the critical factor for inducing such a capacity is not an elaborate architecture, but rather, careful attention to the choice of data and the manner in which it is presented to the model. The most robust capacity for analogical reasoning is induced when networks learn analogies by contrasting abstract relational structures in their input domains, a training method that uses only the input data to force models to learn about important abstract features. Using this technique we demonstrate capacities for complex, visual and symbolic analogy making and generalisation in even the simplest neural network architectures.

Connected through hyperedge HE-1 (induced by source paper "hierarchical rule induction network for abstract visual reasoning"):
  with members:
    - "learning perceptual inference by contrasting"
    - "raven a dataset for relational and analogical visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "iq of neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "are disentangled representations helpful for abstract visual reasoning"):
  with members:
    - "learning disentangled representations with reference based variational autoencoders"
    - "disentangling factors of variation using few labels"
    - "on the fairness of disentangled representations"
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "competitive training of mixtures of independent deep generative models"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"

Connected through hyperedge HE-3 (induced by source paper "what can neural networks reason about"):
  with members:
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "raven a dataset for relational and analogical visual reasoning"
    - "analysing mathematical reasoning abilities of neural models"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the turing completeness of modern neural network architectures"
    - "learning and generalization in overparameterized neural networks going beyond two layers"

Connected through hyperedge HE-4 (induced by source paper "learning perceptual inference by contrasting"):
  with members:
    - "raven a dataset for relational and analogical visual reasoning"
    - "improving generalization for abstract reasoning tasks using disentangled feature representations"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "contrastive learning for image captioning"
    - "iq of neural networks"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-5 (induced by source paper "modeling gestalt visual reasoning on the raven s progressive matrices intelligence test using generative image inpainting techniques"):
  with members:
    - "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"
    - "measuring abstract reasoning in neural networks"
    - "generative image inpainting with contextual attention"
    - "generative face completion"
    - "deep image prior"

Connected through hyperedge HE-6 (induced by source paper "the relational processing limits of classic and contemporary neural network models of language processing"):
  with members:
    - "relational inductive biases deep learning and graph networks"
    - "deep learning a critical appraisal"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "a simple neural network module for relational reasoning"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "adversarial examples for evaluating reading comprehension systems"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "building machines that learn and think like people"

Connected through hyperedge HE-7 (induced by source paper "abstract reasoning with distracting features"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "raven a dataset for relational and analogical visual reasoning"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "improving generalization for abstract reasoning tasks using disentangled feature representations"
    - "learning to reweight examples for robust deep learning"
    - "information constraints on auto encoding variational bayes"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (induced by source paper "training neural networks to encode symbols enables combinatorial generalization"):
  with members:
    - "colorless green recurrent networks dream hierarchically"
    - "measuring abstract reasoning in neural networks"
    - "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"

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)