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: "deep learning recommendation model for personalization and recommendation systems"
  Abstract: With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks differ significantly from other deep learning networks due to their need to handle categorical features and are not well studied or understood. In this paper, we develop a state-of-the-art deep learning recommendation model (DLRM) and provide its implementation in both PyTorch and Caffe2 frameworks. In addition, we design a specialized parallelization scheme utilizing model parallelism on the embedding tables to mitigate memory constraints while exploiting data parallelism to scale-out compute from the fully-connected layers. We compare DLRM against existing recommendation models and characterize its performance on the Big Basin AI platform, demonstrating its usefulness as a benchmark for future algorithmic experimentation and system co-design.

Connected through hyperedge HE-1 (induced by source paper "strength from weakness fast learning using weak supervision"):
  with members:
    - "weakly supervised disentanglement by pairwise similarities"
    - "generative discriminative complementary learning"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "self supervised learning of pretext invariant representations"
    - "unsupervised representation learning by predicting image rotations"
    - "exploring the limits of weakly supervised pretraining"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "logistic regression the importance of being improper"

Connected through hyperedge HE-2 (induced by source paper "deeprecsys a system for optimizing end to end at scale neural recommendation inference"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "mixed dimension embeddings with application to memory efficient recommendation systems"
    - "mlperf inference benchmark"
    - "prema a predictive multi task scheduling algorithm for preemptible neural processing units"
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "compositional embeddings using complementary partitions for memory efficient recommendation systems"
    - "mlperf training benchmark"
    - "benchmarking tpu gpu and cpu platforms for deep learning"

Connected through hyperedge HE-3 (induced by source paper "handling position bias for unbiased learning to rank in hotels search"):
  with members:
    - "estimating position bias without intrusive interventions"
    - "position bias estimation for unbiased learning to rank in ecommerce search"
    - "unbiased learning to rank with unbiased propensity estimation"
    - "applying deep learning to airbnb search"
    - "direct estimation of position bias for unbiased learning to rank without intervention"
    - "consistent position bias estimation without online interventions for learning to rank"
    - "visual discovery at pinterest"
    - "related pins at pinterest the evolution of a real world recommender system"

Connected through hyperedge HE-4 (induced by source paper "shadowsync performing synchronization in the background for highly scalable distributed training"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "stochastic gradient push for distributed deep learning"
    - "scaling sgd batch size to 32k for imagenet training"
    - "large batch training of convolutional networks"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "asynchronous stochastic gradient descent with delay compensation"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-5 (induced by source paper "developing a recommendation benchmark for mlperf training and inference"):
  with members:
    - "scalable realistic recommendation datasets through fractal expansions"
    - "mlperf inference benchmark"
    - "deep cross network for ad click predictions"

Connected through hyperedge HE-6 (induced by source paper "mlperf training benchmark"):
  with members:
    - "scalable realistic recommendation datasets through fractal expansions"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "scalable methods for 8 bit training of neural networks"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "nvidia tensor core programmability performance precision"
    - "image classification at supercomputer scale"

Connected through hyperedge HE-7 (induced by source paper "mixed dimension embeddings with application to memory efficient recommendation systems"):
  with members:
    - "on the dimensionality of embeddings for sparse features and data"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "saec similarity aware embedding compression in recommendation systems"
    - "practice on long sequential user behavior modeling for click through rate prediction"
    - "tensorized embedding layers for efficient model compression"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "block based singular value decomposition approach to matrix factorization for recommender systems"
    - "on the difficulty of evaluating baselines a study on recommender systems"

Connected through hyperedge HE-8 (induced by source paper "the architectural implications of facebook s dnn based personalized recommendation"):
  with members:
    - "on the dimensionality of embeddings for sparse features and data"
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "mlperf training benchmark"
    - "recnmp accelerating personalized recommendation with near memory processing"
    - "bandana using non volatile memory for storing deep learning models"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "tbd benchmarking and analyzing deep neural network training"
    - "imagenet training in minutes"

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.IR(Information Retrieval)