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: "feature hashing for large scale multitask learning"
  Abstract: Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation. In this paper we provide exponential tail bounds for feature hashing and show that the interaction between random subspaces is negligible with high probability. We demonstrate the feasibility of this approach with experimental results for a new use case -- multitask learning with hundreds of thousands of tasks.

Connected through hyperedge HE-1 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition"):
  with members:
    - "hallucinating optical flow features for video classification"
    - "few shot learning via saliency guided hallucination of samples"
    - "assemblenet searching for multi stream neural connectivity in video architectures"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "deeply supervised salient object detection with short connections"
    - "learning discriminative video representations using adversarial perturbations"
    - "evolving space time neural architectures for videos"
    - "a deeper look at power normalizations"

Connected through hyperedge HE-2 (induced by source paper "learning multi granular quantized embeddings for large vocab categorical features in recommender systems"):
  with members:
    - "albert a lite bert for self supervised learning of language representations"
    - "differentiable product quantization for end to end embedding compression"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "self attentive sequential recommendation"
    - "adaptive input representations for neural language modeling"
    - "neural collaborative filtering"
    - "translation based recommendation"
    - "learning k way d dimensional discrete code for compact embedding representations"

Connected through hyperedge HE-3 (induced by source paper "improved subsampled randomized hadamard transform for linear svm"):
  with members:
    - "faster neural network training with approximate tensor operations"
    - "fully understanding the hashing trick"
    - "provable deterministic leverage score sampling"
    - "recovering the optimal solution by dual random projection"
    - "low rank approximation and regression in input sparsity time"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "randomized algorithms for matrices and data"
    - "random projections for k means clustering"

Connected through hyperedge HE-4 (induced by source paper "highlight every step knowledge distillation via collaborative teaching"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "model compression via distillation and quantization"
    - "private model compression via knowledge distillation"
    - "amalgamating knowledge towards comprehensive classification"
    - "improving fast segmentation with teacher student learning"
    - "label refinery improving imagenet classification through label progression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-5 (induced by source paper "provable filter pruning for efficient neural networks"):
  with members:
    - "revisiting hard thresholding for dnn pruning"
    - "sipping neural networks sensitivity informed provable pruning of neural networks"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "learning filter basis for convolutional neural network compression"
    - "the state of sparsity in deep neural networks"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "learning to prune filters in convolutional neural networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"

Connected through hyperedge HE-6 (induced by source paper "structured multi hashing for model compression"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "searching for mobilenetv3"
    - "model compression by entropy penalized reparameterization"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "non discriminative data or weak model on the relative importance of data and model resolution"
    - "measuring the intrinsic dimension of objective landscapes"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-7 (induced by source paper "deep learning at the edge"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "pact parameterized clipping activation for quantized neural networks"
    - "deep learning in mobile and wireless networking a survey"
    - "deep learning for iot big data and streaming analytics a survey"
    - "wrpn wide reduced precision networks"
    - "hashnet deep learning to hash by continuation"
    - "distributed deep neural networks over the cloud the edge and end devices"

Connected through hyperedge HE-8 (induced by source paper "accelerating generalized linear models with mlweaving a one size fits all system for any precision learning technical report"):
  with members:
    - "high accuracy low precision training"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "in rdbms hardware acceleration of advanced analytics"
    - "bismo a scalable bit serial matrix multiplication overlay for reconfigurable computing"
    - "vibnn hardware acceleration of bayesian neural networks"
    - "e rnn design optimization for efficient recurrent neural networks in fpgas"
    - "neural cache bit serial in cache acceleration of deep neural networks"
    - "in datacenter performance analysis of a tensor processing unit"

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.AI(Artificial Intelligence)