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: "muppet mapreduce style processing of fast data"
  Abstract: MapReduce has emerged as a popular method to process big data. In the past few years, however, not just big data, but fast data has also exploded in volume and availability. Examples of such data include sensor data streams, the Twitter Firehose, and Facebook updates. Numerous applications must process fast data. Can we provide a MapReduce-style framework so that developers can quickly write such applications and execute them over a cluster of machines, to achieve low latency and high scalability? In this paper we report on our investigation of this question, as carried out at Kosmix and WalmartLabs. We describe MapUpdate, a framework like MapReduce, but specifically developed for fast data. We describe Muppet, our implementation of MapUpdate. Throughout the description we highlight the key challenges, argue why MapReduce is not well suited to address them, and briefly describe our current solutions. Finally, we describe our experience and lessons learned with Muppet, which has been used extensively at Kosmix and WalmartLabs to power a broad range of applications in social media and e-commerce.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive survey on parallelization and elasticity in stream processing"):
  with members:
    - "p4cep towards in network complex event processing"
    - "toward reliable and rapid elasticity for streaming dataflows on clouds"
    - "minimizing communication overhead in window based parallel complex event processing"
    - "event forecasting with pattern markov chains"
    - "distributed data stream processing and edge computing a survey on resource elasticity and future directions"
    - "spectre supporting consumption policies in window based parallel complex event processing"
    - "integrative dynamic reconfiguration in a parallel stream processing engine"
    - "when two choices are not enough balancing at scale in distributed stream processing"

Connected through hyperedge HE-2 (induced by source paper "online machine learning in big data streams"):
  with members:
    - "computing web scale topic models using an asynchronous parameter server"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "clipper a low latency online prediction serving system"
    - "xgboost a scalable tree boosting system"
    - "general factorization framework for context aware recommendations"
    - "vht vertical hoeffding tree"
    - "evaluation of recommender systems in streaming environments"
    - "optimal and adaptive algorithms for online boosting"

Connected through hyperedge HE-3 (induced by source paper "model free control for distributed stream data processing using deep reinforcement learning"):
  with members:
    - "continuous deep q learning with model based acceleration"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "benchmarking deep reinforcement learning for continuous control"
    - "deep reinforcement learning in large discrete action spaces"
    - "continuous control with deep reinforcement learning"
    - "language understanding for text based games using deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-4 (induced by source paper "runtime optimization of join location in parallel data management systems"):
  with members:
    - "program transformations for asynchronous and batched query submission"
    - "automatic partitioning of database applications"
    - "cascading map side joins over hbase for scalable join processing"

Connected through hyperedge HE-5 (induced by source paper "elasticutor rapid elasticity for realtime stateful stream processing"):
  with members:
    - "r storm resource aware scheduling in storm"
    - "optimal operator state migration for elastic data stream processing"

Connected through hyperedge HE-6 (induced by source paper "empirical big data research a systematic literature mapping"):
  with members:
    - "performance evaluation of a natural language processing approach applied in white collar crime investigation"
    - "performance benefits of datampi a case study with bigdatabench"
    - "multilevel hierarchical kernel spectral clustering for real life large scale complex networks"
    - "graphical evolutionary game for information diffusion over social networks"
    - "the family of mapreduce and large scale data processing systems"
    - "optimizing i o for big array analytics"
    - "pf ola a high performance framework for parallel on line aggregation"
    - "can the elephants handle the nosql onslaught"

Connected through hyperedge HE-7 (induced by source paper "drs dynamic resource scheduling for real time analytics over fast streams"):
  with members:
    - "optimal operator state migration for elastic data stream processing"

Connected through hyperedge HE-8 (induced by source paper "fast data in the era of big data twitter s real time related query suggestion architecture"):
  with members:
    - "the unified logging infrastructure for data analytics at twitter"

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)