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: "dimmwitted a study of main memory statistical analytics"
  Abstract: We perform the first study of the tradeoff space of access methods and replication to support statistical analytics using first-order methods executed in the main memory of a Non-Uniform Memory Access (NUMA) machine. Statistical analytics systems differ from conventional SQL-analytics in the amount and types of memory incoherence they can tolerate. Our goal is to understand tradeoffs in accessing the data in row- or column-order and at what granularity one should share the model and data for a statistical task. We study this new tradeoff space, and discover there are tradeoffs between hardware and statistical efficiency. We argue that our tradeoff study may provide valuable information for designers of analytics engines: for each system we consider, our prototype engine can run at least one popular task at least 100x faster. We conduct our study across five architectures using popular models including SVMs, logistic regression, Gibbs sampling, and neural networks.

Connected through hyperedge HE-1 (induced by source paper "distributed learning of deep neural networks using independent subnet training"):
  with members:
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "mlsys the new frontier of machine learning systems"
    - "large batch training for lstm and beyond"
    - "communication efficient distributed sgd with sketching"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "inefficiency of k fac for large batch size training"
    - "survey of dropout methods for deep neural networks"
    - "on the ineffectiveness of variance reduced optimization for deep learning"

Connected through hyperedge HE-2 (induced by source paper "blinkml efficient maximum likelihood estimation with probabilistic guarantees"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "verdictdb universalizing approximate query processing"
    - "measuring the effects of data parallelism on neural network training"
    - "hemingway modeling distributed optimization algorithms"
    - "unbiased estimates for linear regression via volume sampling"
    - "database learning toward a database that becomes smarter every time"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-3 (induced by source paper "crossbow scaling deep learning with small batch sizes on multi gpu servers"):
  with members:
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "three factors influencing minima in sgd"
    - "revisiting small batch training for deep neural networks"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "measuring the effects of data parallelism on neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "ray a distributed framework for emerging ai applications"

Connected through hyperedge HE-4 (induced by source paper "heterogeneity aware asynchronous decentralized training"):
  with members:
    - "supporting very large models using automatic dataflow graph partitioning"
    - "hop heterogeneity aware decentralized training"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "communication compression for decentralized training"
    - "beyond data and model parallelism for deep neural networks"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "hop heterogeneity aware decentralized training"):
  with members:
    - "communication compression for decentralized training"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "asap asynchronous approximate data parallel computation"
    - "tensorflow a system for large scale machine learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "syscd a system aware parallel coordinate descent algorithm"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "elastic cocoa scaling in to improve convergence"
    - "snap ml a hierarchical framework for machine learning"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "parallel coordinate descent for l1 regularized loss minimization"

Connected through hyperedge HE-8 (induced by source paper "an experimental evaluation of large scale gbdt systems"):
  with members:
    - "moment based quantile sketches for efficient high cardinality aggregation queries"
    - "deep forest towards an alternative to deep neural networks"
    - "optimal quantile approximation in streams"
    - "xgboost a scalable tree boosting system"
    - "robust logitboost and adaptive base class abc logitboost"

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.DB(Databases)