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: "multi scale recognition with dag cnns"
  Abstract: We explore multi-scale convolutional neural nets (CNNs) for image classification. Contemporary approaches extract features from a single output layer. By extracting features from multiple layers, one can simultaneously reason about high, mid, and low-level features during classification. The resulting multi-scale architecture can itself be seen as a feed-forward model that is structured as a directed acyclic graph (DAG-CNNs). We use DAG-CNNs to learn a set of multiscale features that can be effectively shared between coarse and fine-grained classification tasks. While fine-tuning such models helps performance, we show that even "off-the-self" multiscale features perform quite well. We present extensive analysis and demonstrate state-of-the-art classification performance on three standard scene benchmarks (SUN397, MIT67, and Scene15). In terms of the heavily benchmarked MIT67 and Scene15 datasets, our results reduce the lowest previously-reported error by 23.9% and 9.5%, respectively.

Connected through hyperedge HE-1 (induced by source paper "convolutional networks with dense connectivity"):
  with members:
    - "log densenet how to sparsify a densenet"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "condensenet an efficient densenet using learned group convolutions"
    - "resnet in resnet generalizing residual architectures"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-2 (induced by source paper "growing a brain fine tuning by increasing model capacity"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "progressive neural networks"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "a growing long term episodic semantic memory"
    - "one shot learning with memory augmented neural networks"
    - "learning feed forward one shot learners"
    - "cross stitch networks for multi task learning"
    - "what makes imagenet good for transfer learning"

Connected through hyperedge HE-3 (induced by source paper "fosnet an end to end trainable deep neural network for scene recognition"):
  with members:
    - "from volcano to toyshop adaptive discriminative region discovery for scene recognition"
    - "partial convolution based padding"
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "a robust indoor scene recognition method based on sparse representation"
    - "learnable pooling with context gating for video classification"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "squeeze and excitation networks"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"

Connected through hyperedge HE-4 (induced by source paper "semantic fisher scores for task transfer using objects to classify scenes"):
  with members:
    - "locally supervised deep hybrid model for scene recognition"
    - "compositional model based fisher vector coding for image classification"
    - "label embedding for image classification"
    - "deep filter banks for texture recognition description and segmentation"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "enhanced transfer learning with imagenet trained classification layer"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "learning more universal representations for transfer learning"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "a growing long term episodic semantic memory"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning visual features from large weakly supervised data"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "depth augmented networks with optimal fine tuning"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "learning more universal representations for transfer learning"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "a growing long term episodic semantic memory"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning visual features from large weakly supervised data"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "big little net an efficient multi scale feature representation for visual and speech recognition"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "stochastic downsampling for cost adjustable inference and improved regularization in convolutional networks"
    - "cbam convolutional block attention module"
    - "skipnet learning dynamic routing in convolutional networks"
    - "progressive neural architecture search"
    - "english conversational telephone speech recognition by humans and machines"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-8 (induced by source paper "learning deep structured multi scale features using attention gated crfs for contour prediction"):
  with members:
    - "learning cross modal deep representations for robust pedestrian detection"
    - "richer convolutional features for edge detection"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "crafting gbd net for object detection"
    - "crf cnn modeling structured information in human pose estimation"
    - "semicontour a semi supervised learning approach for contour detection"

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)