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: "visual concepts and compositional voting"
  Abstract: It is very attractive to formulate vision in terms of pattern theory \cite{Mumford2010pattern}, where patterns are defined hierarchically by compositions of elementary building blocks. But applying pattern theory to real world images is currently less successful than discriminative methods such as deep networks. Deep networks, however, are black-boxes which are hard to interpret and can easily be fooled by adding occluding objects. It is natural to wonder whether by better understanding deep networks we can extract building blocks which can be used to develop pattern theoretic models. This motivates us to study the internal representations of a deep network using vehicle images from the PASCAL3D+ dataset. We use clustering algorithms to study the population activities of the features and extract a set of visual concepts which we show are visually tight and correspond to semantic parts of vehicles. To analyze this we annotate these vehicles by their semantic parts to create a new dataset, VehicleSemanticParts, and evaluate visual concepts as unsupervised part detectors. We show that visual concepts perform fairly well but are outperformed by supervised discriminative methods such as Support Vector Machines (SVM). We next give a more detailed analysis of visual concepts and how they relate to semantic parts. Following this, we use the visual concepts as building blocks for a simple pattern theoretical model, which we call compositional voting. In this model several visual concepts combine to detect semantic parts. We show that this approach is significantly better than discriminative methods like SVM and deep networks trained specifically for semantic part detection. Finally, we return to studying occlusion by creating an annotated dataset with occlusion, called VehicleOcclusion, and show that compositional voting outperforms even deep networks when the amount of occlusion becomes large.

Connected through hyperedge HE-1 (induced by source paper "compositional convolutional neural networks a deep architecture with innate robustness to partial occlusion"):
  with members:
    - "combining compositional models and deep networks for robust object classification under occlusion"
    - "compositional convolutional networks for robust object classification under occlusion"
    - "tdapnet prototype network with recurrent top down attention for robust object classification under partial occlusion"
    - "greedy structure learning of hierarchical compositional models"
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "interpretable convolutional neural networks"
    - "aognets compositional grammatical architectures for deep learning"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-2 (induced by source paper "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"):
  with members:
    - "assessing bert s syntactic abilities"
    - "human vs muppet a conservative estimate of human performance on the glue benchmark"
    - "non entailed subsequences as a challenge for natural language inference"
    - "annotation artifacts in natural language inference data"
    - "assessing composition in sentence vector representations"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "teaching syntax by adversarial distraction"

Connected through hyperedge HE-3 (induced by source paper "buzz buffer zones for defending adversarial examples in image classification"):
  with members:
    - "muldef multi model based defense against adversarial examples for neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "magnet a two pronged defense against adversarial examples"
    - "countering adversarial images using input transformations"
    - "synthesizing robust adversarial examples"
    - "mitigating adversarial effects through randomization"
    - "mitigating evasion attacks to deep neural networks via region based classification"

Connected through hyperedge HE-4 (induced by source paper "tdapnet prototype network with recurrent top down attention for robust object classification under partial occlusion"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "compositional convolutional networks for robust object classification under occlusion"
    - "robustness of object recognition under extreme occlusion in humans and computational models"
    - "convolutional neural networks with alternately updated clique"
    - "robust classification with convolutional prototype learning"
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "prototypical networks for few shot learning"
    - "learning with rethinking recurrently improving convolutional neural networks through feedback"

Connected through hyperedge HE-5 (induced by source paper "sesamebert attention for anywhere"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "information aggregation for multi head attention with routing by agreement"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "behavior analysis of nli models uncovering the influence of three factors on robustness"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "convolutional self attention network"
    - "stress test evaluation for natural language inference"

Connected through hyperedge HE-6 (induced by source paper "deep nets what have they ever done for vision"):
  with members:
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "the elephant in the room"
    - "deep learning a critical appraisal"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "progressive neural architecture search"
    - "few shot image recognition by predicting parameters from activations"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-7 (induced by source paper "robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks"):
  with members:
    - "bi directional cascade network for perceptual edge detection"
    - "deep predictive coding network for object recognition"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "deep predictive coding network with local recurrent processing for object recognition"
    - "learning long range spatial dependencies with horizontal gated recurrent units"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "can recurrent neural networks warp time"
    - "learning what and where to attend"

Connected through hyperedge HE-8 (induced by source paper "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"):
  with members:
    - "single shot object detection with enriched semantics"
    - "scalenet guiding object proposal generation in supermarkets and beyond"
    - "ssd single shot multibox detector"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "you only look once unified real time object detection"
    - "conditional random fields as recurrent neural networks"
    - "deepm a deep part based model for object detection and semantic part localization"

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)