human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "training wide residual networks for deployment using a single bit for each weight":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse weight activation training")
  Member "superneurons dynamic gpu memory management for training deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding and optimizing packed neural network training for hyper parameter tuning")
  Member "ease ml towards multi tenant resource sharing for machine learning workloads":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantitative overfitting management for human in the loop ml application development with ease ml meter")
  Member "memory efficient implementation of densenets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference")
  Member "comparative study of deep learning software frameworks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "high performance i o for large scale deep learning")
  Member "lifetime based memory management for distributed data processing systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pangea monolithic distributed storage for data analytics")
  Member "database meets deep learning challenges and opportunities":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "facilitating sql query composition and analysis")
  Member "eie efficient inference engine on compressed deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications")

Question: Please tell me which class the source paper 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)