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: "scarff a scalable framework for streaming credit card fraud detection with spark"
  Abstract: Abstract   The expansion of the electronic commerce, together with an increasing confidence of customers in electronic payments, makes of fraud detection a critical factor. Detecting frauds in (nearly) real time setting demands the design and the implementation of scalable learning techniques able to ingest and analyse massive amounts of streaming data. Recent advances in analytics and the availability of open source solutions for Big Data storage and processing open new perspectives to the fraud detection field. In this paper we present a Scalable Real-time Fraud Finder (SCARFF) which integrates Big Data tools (Kafka, Spark and Cassandra) with a machine learning approach which deals with imbalance, nonstationarity and feedback latency. Experimental results on a massive dataset of real credit card transactions show that this framework is scalable, efficient and accurate over a big stream of transactions.

Connected through hyperedge HE-1 (induced by source paper "smart data based ensemble for imbalanced big data classification"):
  with members:
    - "catboost unbiased boosting with categorical features"
    - "enabling smart data noise filtering in big data classification"
    - "xgboost a scalable tree boosting system"
    - "mllib machine learning in apache spark"
    - "what will 5g be"
    - "mapreduce is good enough if all you have is a hammer throw away everything that s not a nail"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-2 (induced by source paper "rade resource efficient supervised anomaly detection using decision tree based ensemble methods"):
  with members:
    - "forest packing fast parallel decision forests"
    - "catboost unbiased boosting with categorical features"
    - "xgboost a scalable tree boosting system"
    - "robust logitboost and adaptive base class abc logitboost"

Connected through hyperedge HE-3 (induced by source paper "synthesizing credit card transactions"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning deep object detectors from 3d models"

Connected through hyperedge HE-4 (induced by source paper "streaming active learning strategies for real life credit card fraud detection assessment and visualization"):
  with members:
    - "a new local distance based outlier detection approach for scattered real world data"
    - "smote synthetic minority over sampling technique"

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)