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: "modelhub towards unified data and lifecycle management for deep learning"
  Abstract: Deep learning has improved state-of-the-art results in many important fields, and has been the subject of much research in recent years, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of data management, model sharing, and lifecycle management are largely ignored. Deep learning modeling lifecycle generates a rich set of data artifacts, such as learned parameters and training logs, and comprises of several frequently conducted tasks, e.g., to understand the model behaviors and to try out new models. Dealing with such artifacts and tasks is cumbersome and largely left to the users. This paper describes our vision and implementation of a data and lifecycle management system for deep learning. First, we generalize model exploration and model enumeration queries from commonly conducted tasks by deep learning modelers, and propose a high-level domain specific language (DSL), inspired by SQL, to raise the abstraction level and accelerate the modeling process. To manage the data artifacts, especially the large amount of checkpointed float parameters, we design a novel model versioning system (dlv), and a read-optimized parameter archival storage system (PAS) that minimizes storage footprint and accelerates query workloads without losing accuracy. PAS archives versioned models using deltas in a multi-resolution fashion by separately storing the less significant bits, and features a novel progressive query (inference) evaluation algorithm. Third, we show that archiving versioned models using deltas poses a new dataset versioning problem and we develop efficient algorithms for solving it. We conduct extensive experiments over several real datasets from computer vision domain to show the efficiency of the proposed techniques.

Connected through hyperedge HE-1 (induced by source paper "modelhub ai dissemination platform for deep learning models"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"
    - "dlhub model and data serving for science"
    - "unsupervised odometry and depth learning for endoscopic capsule robots"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "nips not even wrong a systematic review of empirically complete demonstrations of algorithmic effectiveness in the machine learning and artificial intelligence literature"
    - "tensorflow serving flexible high performance ml serving"

Connected through hyperedge HE-2 (induced by source paper "deepbase deep inspection of neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "analyzing and interpreting neural networks for nlp a report on the first blackboxnlp workshop"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "learning to generate reviews and discovering sentiment"
    - "network dissection quantifying interpretability of deep visual representations"
    - "robustfill neural program learning under noisy i o"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"

Connected through hyperedge HE-3 (induced by source paper "big data systems meet machine learning challenges towards big data science as a service"):
  with members:
    - "infrastructure for usable machine learning the stanford dawn project"
    - "f2 reenvisioning data analytics systems"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "declarative machine learning a classification of basic properties and types"
    - "mllib machine learning in apache spark"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "the madlib analytics library or mad skills the sql"
    - "distributed graphlab a framework for machine learning in the cloud"

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)