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: "the unified logging infrastructure for data analytics at twitter"
  Abstract: In recent years, there has been a substantial amount of work on large-scale data analytics using Hadoop-based platforms running on large clusters of commodity machines. A less-explored topic is how those data, dominated by application logs, are collected and structured to begin with. In this paper, we present Twitter's production logging infrastructure and its evolution from application-specific logging to a unified "client events" log format, where messages are captured in common, well-formatted, flexible Thrift messages. Since most analytics tasks consider the user session as the basic unit of analysis, we pre-materialize "session sequences", which are compact summaries that can answer a large class of common queries quickly. The development of this infrastructure has streamlined log collection and data analysis, thereby improving our ability to rapidly experiment and iterate on various aspects of the service.

Connected through hyperedge HE-1 (induced by source paper "machine learning systems for highly distributed and rapidly growing data"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "semi cyclic stochastic gradient descent"
    - "the non iid data quagmire of decentralized machine learning"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "group normalization"
    - "federated learning with non iid data"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "logzip extracting hidden structures via iterative clustering for log compression"):
  with members:
    - "a directed acyclic graph approach to online log parsing"
    - "tools and benchmarks for automated log parsing"
    - "detection of early stage enterprise infection by mining large scale log data"

Connected through hyperedge HE-3 (induced by source paper "tools and benchmarks for automated log parsing"):
  with members:
    - "navigating the data lake with datamaran automatically extracting structure from log datasets"
    - "length matters clustering system log messages using length of words"
    - "detection of early stage enterprise infection by mining large scale log data"

Connected through hyperedge HE-4 (induced by source paper "when hashes met wedges a distributed algorithm for finding high similarity vectors"):
  with members:
    - "practical and optimal lsh for angular distance"
    - "diamond sampling for approximate maximum all pairs dot product mad search"
    - "complexity measures for map reduce and comparison to parallel computing"
    - "dimension independent similarity computation"
    - "compressed matrix multiplication"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "on sparse representations of linear operators and the approximation of matrix products"

Connected through hyperedge HE-5 (induced by source paper "towards geo distributed machine learning"):
  with members:
    - "communication efficient distributed dual coordinate ascent"
    - "a reliable effective terascale linear learning system"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-6 (induced by source paper "fast data in the era of big data twitter s real time related query suggestion architecture"):
  with members:
    - "muppet mapreduce style processing of fast data"

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)