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: "f2 reenvisioning data analytics systems"
  Abstract: Today's data analytics frameworks are intrinsically compute-centric. Key details of analytics execution - work allocation to distributed compute tasks, intermediate data storage, task scheduling, etc. - depend on the pre-determined physical structure of the high-level computation. Unfortunately, this hurts flexibility, performance, and efficiency. We present F2, a new analytics framework that cleanly separates computation from intermediate data. It enables runtime visibility into data via programmable monitoring, and data-driven computation (where intermediate data values drive when and what computation runs) via an event abstraction. Experiments with an F2 prototype on a large cluster using batch, streaming, and graph analytics workloads show that it significantly outperforms state-of-the-art compute-centric engines.

Connected through hyperedge HE-1 (induced by source paper "trims transparent and isolated model sharing for low latency deep learninginference in function as a service environments"):
  with members:
    - "tensorflow serving flexible high performance ml serving"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "clipper a low latency online prediction serving system"
    - "planet photo geolocation with convolutional neural networks"
    - "do the hard stuff first scheduling dependent computations in data analytics clusters"
    - "wide residual networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-2 (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"
    - "modelhub towards unified data and lifecycle management for deep learning"
    - "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.DC(Distributed, Parallel, and Cluster Computing)