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: "convolutional networks and learning invariant to homogeneous multiplicative scalings"
  Abstract: The conventional classification schemes -- notably multinomial logistic regression -- used in conjunction with convolutional networks (convnets) are classical in statistics, designed without consideration for the usual coupling with convnets, stochastic gradient descent, and backpropagation. In the specific application to supervised learning for convnets, a simple scale-invariant classification stage turns out to be more robust than multinomial logistic regression, appears to result in slightly lower errors on several standard test sets, has similar computational costs, and features precise control over the actual rate of learning. "Scale-invariant" means that multiplying the input values by any nonzero scalar leaves the output unchanged.

Connected through hyperedge HE-1 (induced by source paper "learning multimodal representations for unseen activities"):
  with members:
    - "fine grained activity recognition in baseball videos"
    - "joint event detection and description in continuous video streams"
    - "semantic autoencoder for zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "zero shot learning via class conditioned deep generative models"
    - "dense captioning events in videos"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "unseen action recognition with multimodal learning"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "end to end dense video captioning with masked transformer"
    - "joint event detection and description in continuous video streams"
    - "semantic autoencoder for zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "learning deep structure preserving image text embeddings"
    - "deep visual semantic alignments for generating image descriptions"

Connected through hyperedge HE-3 (induced by source paper "deep complex networks"):
  with members:
    - "complex and holographic embeddings of knowledge graphs a comparison"
    - "associative long short term memory"
    - "on complex valued convolutional neural networks"
    - "harmonic networks deep translation and rotation equivariance"
    - "identity mappings in deep residual networks"
    - "conditional image generation with pixelcnn decoders"
    - "recurrent highway networks"
    - "wide residual networks"

Connected through hyperedge HE-4 (induced by source paper "learning deep resnet blocks sequentially using boosting theory"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "identity matters in deep learning"
    - "adanet adaptive structural learning of artificial neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"

Connected through hyperedge HE-5 (induced by source paper "energy based spherical sparse coding"):
  with members:
    - "adam a method for stochastic optimization"
    - "striving for simplicity the all convolutional net"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-6 (induced by source paper "learning high level prior with convolutional neural networks for semantic segmentation"):
  with members:
    - "deeply learning the messages in message passing inference"
    - "efficient piecewise training of deep structured models for semantic segmentation"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "hypercolumns for object segmentation and fine grained localization"
    - "fully convolutional networks for semantic segmentation"
    - "learning generative models with visual attention"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)