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: "fix your classifier the marginal value of training the last weight layer"
  Abstract: Neural networks are commonly used as models for classification for a wide variety of tasks. Typically, a learned affine transformation is placed at the end of such models, yielding a per-class value used for classification. This classifier can have a vast number of parameters, which grows linearly with the number of possible classes, thus requiring increasingly more resources. #R##N#In this work we argue that this classifier can be fixed, up to a global scale constant, with little or no loss of accuracy for most tasks, allowing memory and computational benefits. Moreover, we show that by initializing the classifier with a Hadamard matrix we can speed up inference as well. We discuss the implications for current understanding of neural network models.

Connected through hyperedge HE-1 (induced by source paper "dense classification and implanting for few shot learning"):
  with members:
    - "rethinking feature distribution for loss functions in image classification"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "ring loss convex feature normalization for face recognition"
    - "path aggregation network for instance segmentation"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-2 (induced by source paper "towards learning of filter level heterogeneous compression of convolutional neural networks"):
  with members:
    - "improving neural architecture search image classifiers via ensemble learning"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "regularizing activation distribution for training binarized deep networks"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "nas bench 101 towards reproducible neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"

Connected through hyperedge HE-3 (induced by source paper "a constructive prediction of the generalization error across scales"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "regularized evolution for image classifier architecture search"
    - "on the convergence rate of training recurrent neural networks"
    - "darts differentiable architecture search"

Connected through hyperedge HE-4 (induced by source paper "contextdesc local descriptor augmentation with cross modality context"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "matchable image retrieval by learning from surface reconstruction"
    - "local descriptors optimized for average precision"
    - "learning and matching multi view descriptors for registration of point clouds"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "repeatability is not enough learning affine regions via discriminability"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-5 (induced by source paper "hyperspherical prototype networks"):
  with members:
    - "prototypical metric transfer learning for continuous speech keyword spotting with limited training data"
    - "infinite mixture prototypes for few shot learning"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "deep learning on small datasets without pre training using cosine loss"
    - "decoupled networks"
    - "cosface large margin cosine loss for deep face recognition"
    - "robust classification with convolutional prototype learning"
    - "learning towards minimum hyperspherical energy"

Connected through hyperedge HE-6 (induced by source paper "lca loss change allocation for neural network training"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "measuring the intrinsic dimension of objective landscapes"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "visualizing the loss landscape of neural nets"
    - "critical learning periods in deep neural networks"
    - "the loss surface of deep and wide neural networks"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-7 (induced by source paper "fix your features stationary and maximally discriminative embeddings using regular polytope fixed classifier networks"):
  with members:
    - "decoupled networks"
    - "learning towards minimum hyperspherical energy"
    - "additive margin softmax for face verification"
    - "improving generalization via scalable neighborhood component analysis"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-8 (induced by source paper "fixynn efficient hardware for mobile computer vision via transfer learning"):
  with members:
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "towards closing the energy gap between hog and cnn features for embedded vision"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning multiple visual domains with residual adapters"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"

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)