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: "learning rotation invariant convolutional filters for texture classification"
  Abstract: We present a method for learning discriminative filters using a shallow Convolutional Neural Network (CNN). We encode rotation invariance directly in the model by tying the weights of groups of filters to several rotated versions of the canonical filter in the group. These filters can be used to extract rotation invariant features well-suited for image classification. We test this learning procedure on a texture classification benchmark, where the orientations of the training images differ from those of the test images. We obtain results comparable to the state-of-the-art. Compared to standard shallow CNNs, the proposed method obtains higher classification performance while reducing by an order of magnitude the number of parameters to be learned.

Connected through hyperedge HE-1 (induced by source paper "rest performance improvement of a black box model via rl based spatial transformation"):
  with members:
    - "neural network gradient based learning of black box function interfaces"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "pointwise rotation invariant network with adaptive sampling and 3d spherical voxel convolution"
    - "collaborative deep reinforcement learning for joint object search"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "enhancing feature invariance with learned image transformations for image retrieval"):
  with members:
    - "component based attention for large scale trademark retrieval"
    - "learning transferable architectures for scalable image recognition"
    - "deformable convolutional networks"
    - "fine tuning cnn image retrieval with no human annotation"
    - "a large scale dataset and benchmark for similar trademark retrieval"
    - "proximal policy optimization algorithms"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "general e 2 equivariant steerable cnns"):
  with members:
    - "spherical cnns on unstructured grids"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "learning to convolve a generalized weight tying approach"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "3d g cnns for pulmonary nodule detection"

Connected through hyperedge HE-4 (induced by source paper "lgn cnn a biologically inspired cnn architecture"):
  with members:
    - "fundamentals of recurrent neural network rnn and long short term memory lstm network"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "flip rotate pooling convolution and split dropout on convolution neural networks for image classification"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-5 (induced by source paper "rotation sensitive regression for oriented scene text detection"):
  with members:
    - "textboxes a single shot oriented scene text detector"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "wordsup exploiting word annotations for character based text detection"
    - "east an efficient and accurate scene text detector"
    - "oriented response networks"
    - "single shot text detector with regional attention"
    - "dota a large scale dataset for object detection in aerial images"

Connected through hyperedge HE-6 (induced by source paper "cubenet equivariance to 3d rotation and translation"):
  with members:
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "learning steerable filters for rotation equivariant cnns"
    - "polar transformer networks"
    - "rotation equivariant vector field networks"
    - "oriented response networks"
    - "dynamic routing between capsules"
    - "deep rotation equivariant network"

Connected through hyperedge HE-7 (induced by source paper "tree species identification from bark images using convolutional neural networks"):
  with members:
    - "uav visual teach and repeat using only semantic object features"
    - "multi task learning by deep collaboration and application in facial landmark detection"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "a survey on multi task learning"
    - "relay backpropagation for effective learning of deep convolutional neural networks"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-8 (induced by source paper "transformationally identical and invariant convolutional neural networks through symmetric element operators"):
  with members:
    - "deep sets"
    - "harmonic networks deep translation and rotation equivariance"
    - "permutation equivariant neural networks applied to dynamics prediction"
    - "group equivariant convolutional networks"

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)