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: "iq of neural networks"
  Abstract: IQ tests are an accepted method for assessing human intelligence. The tests consist of several parts that must be solved under a time constraint. Of all the tested abilities, pattern recognition has been found to have the highest correlation with general intelligence. This is primarily because pattern recognition is the ability to find order in a noisy environment, a necessary skill for intelligent agents. In this paper, we propose a convolutional neural network (CNN) model for solving geometric pattern recognition problems. The CNN receives as input multiple ordered input images and outputs the next image according to the pattern. Our CNN is able to solve problems involving rotation, reflection, color, size and shape patterns and score within the top 5% of human performance.

Connected through hyperedge HE-1 (induced by source paper "evaluating the progress of deep learning for visual relational concepts"):
  with members:
    - "v prom a benchmark for visual reasoning using visual progressive matrices"
    - "same different problems strain convolutional neural networks"
    - "a simple neural network module for relational reasoning"
    - "squeeze and excitation networks"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "evaluation of deep learning on an abstract image classification dataset"

Connected through hyperedge HE-2 (induced by source paper "hierarchical rule induction network for abstract visual reasoning"):
  with members:
    - "learning to make analogies by contrasting abstract relational structure"
    - "learning perceptual inference by contrasting"
    - "raven a dataset for relational and analogical visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "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-3 (induced by source paper "v prom a benchmark for visual reasoning using visual progressive matrices"):
  with members:
    - "visual question answering as a meta learning task"
    - "one shot visual imitation learning via meta learning"
    - "learning generalized reactive policies using deep neural networks"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "a simple neural network module for relational reasoning"
    - "an analysis of visual question answering algorithms"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "learning robust rewards with adversarial inverse reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "raven a dataset for relational and analogical visual reasoning"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "compositional attention networks for machine reasoning"
    - "visual question reasoning on general dependency tree"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "learning perceptual inference by contrasting"):
  with members:
    - "learning to make analogies by contrasting abstract relational structure"
    - "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"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-6 (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"
    - "measuring abstract reasoning in neural networks"
    - "generative image inpainting with contextual attention"
    - "generative face completion"

Connected through hyperedge HE-7 (induced by source paper "abstract reasoning with distracting features"):
  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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "measuring abstract reasoning in neural networks"):
  with members:
    - "deep learning a critical appraisal"
    - "an empirical evaluation of visual question answering for novel objects"
    - "building machines that learn and think for themselves commentary on lake et al behavioral and brain sciences 2017"
    - "discovering objects and their relations from entangled scene representations"
    - "measuring the tendency of cnns to learn surface statistical regularities"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "understanding deep learning requires rethinking generalization"

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)