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: "efficient memory management for gpu based deep learning systems"
  Abstract: GPU (graphics processing unit) has been used for many data-intensive applications. Among them, deep learning systems are one of the most important consumer systems for GPU nowadays. As deep learning applications impose deeper and larger models in order to achieve higher accuracy, memory management becomes an important research topic for deep learning systems, given that GPU has limited memory size. Many approaches have been proposed towards this issue, e.g., model compression and memory swapping. However, they either degrade the model accuracy or require a lot of manual intervention. In this paper, we propose two orthogonal approaches to reduce the memory cost from the system perspective. Our approaches are transparent to the models, and thus do not affect the model accuracy. They are achieved by exploiting the iterative nature of the training algorithm of deep learning to derive the lifetime and read/write order of all variables. With the lifetime semantics, we are able to implement a memory pool with minimal fragments. However, the optimization problem is NP-complete. We propose a heuristic algorithm that reduces up to 13.3% of memory compared with Nvidia's default memory pool with equal time complexity. With the read/write semantics, the variables that are not in use can be swapped out from GPU to CPU to reduce the memory footprint. We propose multiple swapping strategies to automatically decide which variable to swap and when to swap out (in), which reduces the memory cost by up to 34.2% without communication overhead.

Connected through hyperedge HE-1 (induced by source paper "understanding and optimizing packed neural network training for hyper parameter tuning"):
  with members:
    - "hypersched dynamic resource reallocation for model development on a deadline"
    - "crossbow scaling deep learning with small batch sizes on multi gpu servers"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "themis fair and efficient gpu cluster scheduling for machine learning workloads"
    - "exploiting reuse in pipeline aware hyperparameter tuning"
    - "analysis of large scale multi tenant gpu clusters for dnn training workloads"
    - "energy and policy considerations for deep learning in nlp"
    - "green ai"

Connected through hyperedge HE-2 (induced by source paper "streaming convolutional neural networks for end to end learning with multi megapixel images"):
  with members:
    - "processing megapixel images with deep attention sampling models"
    - "predicting breast tumor proliferation from whole slide images the tupac16 challenge"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "neural image compression for gigapixel histopathology image analysis"
    - "attention based deep multiple instance learning"
    - "reinforced auto zoom net towards accurate and fast breast cancer segmentation in whole slide images"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-3 (induced by source paper "specnet spectral domain convolutional neural network"):
  with members:
    - "exploiting kernel sparsity and entropy for interpretable cnn compression"
    - "sort second order response transform for visual recognition"
    - "memory efficient implementation of densenets"
    - "wavelet convolutional neural networks for texture classification"
    - "the power of sparsity in convolutional neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "learning structured sparsity in deep neural networks"

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.DC(Distributed, Parallel, and Cluster Computing)