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: "bandana using non volatile memory for storing deep learning models"
  Abstract: Typical large-scale recommender systems use deep learning models that are stored on a large amount of DRAM. These models often rely on embeddings, which consume most of the required memory. We present Bandana, a storage system that reduces the DRAM footprint of embeddings, by using Non-volatile Memory (NVM) as the primary storage medium, with a small amount of DRAM as cache. The main challenge in storing embeddings on NVM is its limited read bandwidth compared to DRAM. Bandana uses two primary techniques to address this limitation: first, it stores embedding vectors that are likely to be read together in the same physical location, using hypergraph partitioning, and second, it decides the number of embedding vectors to cache in DRAM by simulating dozens of small caches. These techniques allow Bandana to increase the effective read bandwidth of NVM by 2-3x and thereby significantly reduce the total cost of ownership.

Connected through hyperedge HE-1 (induced by source paper "distributed hierarchical gpu parameter server for massive scale deep learning ads systems"):
  with members:
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "the case for learned index structures"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "product based neural networks for user response prediction"

Connected through hyperedge HE-2 (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"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "exploiting parallelism opportunities with deep learning frameworks"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "auto tuning tensorflow threading model for cpu backend"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "recnmp accelerating personalized recommendation with near memory processing"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "basic performance measurements of the intel optane dc persistent memory module"
    - "deepfm an end to end wide deep learning framework for ctr prediction"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "competitive analysis system for theatrical movie releases based on movie trailer deep video representation"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scnn an accelerator for compressed sparse convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "glow graph lowering compiler techniques for neural networks"
    - "exploring the limits of weakly supervised pretraining"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "rosetta large scale system for text detection and recognition in images"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "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.LG(Machine Learning)