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: "discovering internal representations from object cnns using population encoding"
  Abstract: In this paper, we provide a method for understanding the internal representations of Convolutional Neural Networks (CNNs) trained on objects. We hypothesize that the information is distributed across multiple neuronal responses and propose a simple clustering technique to extract this information, which we call \emph{population encoding}. The population encoding technique looks into the entrails of an object-CNN at multiple layers of the network and shows the implicit presence of mid-level object part semantics distributed in the neuronal responses. Our qualitative visualizations show that population encoding can extract mid-level image patches that are visually tighter than the patches that produce high single-filter activations. Moreover, our comprehensive quantitative experiments using the object key point annotations from the PASCAL3D+ dataset corroborate the visualizations by demonstrating the superiority of population encoding over single-filter detectors, in the task of object-part detection. We also perform some preliminary experiments where we uncover the compositional relations between the adjacent layers using the parts detected by population encoding clusters. Finally, based on the insights gained from this work, we point to various new directions which will enable us to have a better understanding of the CNN's internal representations.

Connected through hyperedge HE-1 (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-2 (induced by source paper "class subset selection for transfer learning using submodularity"):
  with members:
    - "what makes imagenet good for transfer learning"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "context encoders feature learning by inpainting"
    - "unsupervised visual representation learning by context prediction"
    - "inverting visual representations with convolutional networks"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image recognition"
    - "understanding deep features with computer generated imagery"

Connected through hyperedge HE-3 (induced by source paper "learning robust object recognition using composed scenes from generative models"):
  with members:
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "generative image modeling using style and structure adversarial networks"
    - "shapenet an information rich 3d model repository"
    - "caffe convolutional architecture for fast feature embedding"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "geometric neural phrase pooling modeling the spatial co occurrence of neurons"):
  with members:
    - "disturblabel regularizing cnn on the loss layer"
    - "wide residual networks"
    - "interactive inter layer activeness propagation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "training very deep networks"

Connected through hyperedge HE-5 (induced by source paper "object recognition with and without objects"):
  with members:
    - "identity mappings in deep residual networks"
    - "what makes imagenet good for transfer learning"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "relaxed multiple instance svm with application to object discovery"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "going deeper with convolutions"

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)