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: "raven a dataset for relational and analogical visual reasoning"
  Abstract: Dramatic progress has been witnessed in basic vision tasks involving low-level perception, such as object recognition, detection, and tracking. Unfortunately, there is still an enormous performance gap between artificial vision systems and human intelligence in terms of higher-level vision problems, especially ones involving reasoning. Earlier attempts in equipping machines with high-level reasoning have hovered around Visual Question Answering (VQA), one typical task associating vision and language understanding. In this work, we propose a new dataset, built in the context of Raven's Progressive Matrices (RPM) and aimed at lifting machine intelligence by associating vision with structural, relational, and analogical reasoning in a hierarchical representation. Unlike previous works in measuring abstract reasoning using RPM, we establish a semantic link between vision and reasoning by providing structure representation. This addition enables a new type of abstract reasoning by jointly operating on the structure representation. Machine reasoning ability using modern computer vision is evaluated in this newly proposed dataset. Additionally, we also provide human performance as a reference. Finally, we show consistent improvement across all models by incorporating a simple neural module that combines visual understanding and structure reasoning.

Connected through hyperedge HE-1 (induced by source paper "solving raven s progressive matrices with neural networks"):
  with members:
    - "learning perceptual inference by contrasting"
    - "deep spectral clustering using dual autoencoder network"
    - "explainable video action reasoning via prior knowledge and state transitions"
    - "rethinking imagenet pre training"
    - "from recognition to cognition visual commonsense reasoning"
    - "deep clustering for unsupervised learning of visual features"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "quo vadis action recognition a new model and the kinetics 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"
    - "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-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"
    - "learning to make analogies by contrasting abstract relational structure"
    - "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:
    - "learning to make analogies by contrasting abstract relational structure"
    - "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:
    - "learning to make analogies by contrasting abstract relational structure"
    - "are disentangled representations helpful for abstract 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 "attention on abstract visual reasoning"):
  with members:
    - "visualbert a simple and performant baseline for vision and language"
    - "deep contextualized word representations"
    - "compositional attention networks for machine reasoning"
    - "improving generalization for abstract reasoning tasks using disentangled feature representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "squad 100 000 questions for machine comprehension of text"

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"
    - "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 "theory based causal transfer integrating instance level induction and abstract level structure learning"):
  with members:
    - "learning perceptual inference by contrasting"
    - "a study on overfitting in deep reinforcement learning"
    - "formalizing neurath s ship approximate algorithms for online causal learning"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "proximal policy optimization algorithms"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "asynchronous methods for deep reinforcement learning"
    - "prioritized experience replay"

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.CV(Computer Vision and Pattern Recognition)