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: "generalized max pooling"
  Abstract: State-of-the-art patch-based image representations involve a pooling operation that aggregates statistics computed from local descriptors. Standard pooling operations include sum- and max-pooling. Sum-pooling lacks discriminability because the resulting representation is strongly influenced by frequent yet often uninformative descriptors, but only weakly influenced by rare yet potentially highly-informative ones. Max-pooling equalizes the influence of frequent and rare descriptors but is only applicable to representations that rely on count statistics, such as the bag-of-visual-words (BOV) and its soft- and sparse-coding extensions. We propose a novel pooling mechanism that achieves the same effect as max-pooling but is applicable beyond the BOV and especially to the state-of-the-art Fisher Vector -- hence the name Generalized Max Pooling (GMP). It involves equalizing the similarity between each patch and the pooled representation, which is shown to be equivalent to re-weighting the per-patch statistics. We show on five public image classification benchmarks that the proposed GMP can lead to significant performance gains with respect to heuristic alternatives.

Connected through hyperedge HE-1 (induced by source paper "fine grained visual textual representation learning"):
  with members:
    - "one shot fine grained instance retrieval"
    - "fine grained image classification via combining vision and language"
    - "low rank bilinear pooling for fine grained classification"
    - "learning deep representations of fine grained visual descriptions"
    - "generative adversarial text to image synthesis"
    - "weakly supervised fine grained categorization with part based image representation"
    - "hierarchical question image co attention for visual question answering"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"

Connected through hyperedge HE-2 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "3d local features for direct pairwise registration"
    - "view n gram network for 3d object retrieval"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-3 (induced by source paper "simultaneous feature aggregating and hashing for compact binary code learning"):
  with members:
    - "on device scalable image based localization via prioritized cascade search and fast one many ransac"
    - "deep supervised discrete hashing"
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "embedding based on function approximation for large scale image search"
    - "a survey on learning to hash"
    - "learning to hash with binary deep neural network"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep semantic ranking based hashing for multi label image retrieval"

Connected through hyperedge HE-4 (induced by source paper "deep generalized max pooling"):
  with members:
    - "deep adaptive learning for writer identification based on single handwritten word images"
    - "semi supervised feature learning for improving writer identification"
    - "on the convergence of adam and beyond"
    - "precision learning towards use of known operators in neural networks"
    - "in defense of the triplet loss for person re identification"
    - "unsupervised feature learning for writer identification and writer retrieval"
    - "encoding cnn activations for writer recognition"
    - "convolutional neural networks for font classification"

Connected through hyperedge HE-5 (induced by source paper "micro expression action unit detection withspatio temporal adaptive pooling"):
  with members:
    - "less is more micro expression recognition from video using apex frame"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "focal loss for dense object detection"
    - "deep extreme multi label learning"
    - "optimizing filter size in convolutional neural networks for facial action unit recognition"
    - "adascan adaptive scan pooling in deep convolutional neural networks for human action recognition in videos"
    - "contextual action recognition with r cnn"

Connected through hyperedge HE-6 (induced by source paper "class specific residual constraint non negative representation for pattern classification"):
  with members:
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "structured occlusion coding for robust face recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "fine grained visual classification of aircraft"

Connected through hyperedge HE-7 (induced by source paper "object part attention model for fine grained image classification"):
  with members:
    - "diversified visual attention networks for fine grained object classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "weakly supervised fine grained categorization with part based image representation"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "interactive inter layer activeness propagation"
    - "mining discriminative triplets of patches for fine grained classification"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"
    - "fine grained image classification by exploring bipartite graph labels"

Connected through hyperedge HE-8 (induced by source paper "from selective deep convolutional features to compact binary representations for image retrieval"):
  with members:
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "simultaneous feature aggregating and hashing for large scale image search"
    - "exploiting depth from single monocular images for object detection and semantic segmentation"
    - "selective deep convolutional features for image retrieval"
    - "fine tuning cnn image retrieval with no human annotation"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "deep image retrieval learning global representations for image search"
    - "embedding based on function approximation for large scale image search"

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)