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: "learning to filter spam e mail a comparison of a naive bayesian and a memory based approach"
  Abstract: We investigate the performance of two machine learning algorithms in the context of anti-spam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a need for reliable anti-spam filters. Filters of this type have so far been based mostly on keyword patterns that are constructed by hand and perform poorly. The Naive Bayesian classifier has recently been suggested as an effective method to construct automatically anti-spam filters with superior performance. We investigate thoroughly the performance of the Naive Bayesian filter on a publicly available corpus, contributing towards standard benchmarks. At the same time, we compare the performance of the Naive Bayesian filter to an alternative memory-based learning approach, after introducing suitable cost-sensitive evaluation measures. Both methods achieve very accurate spam filtering, outperforming clearly the keyword-based filter of a widely used e-mail reader.

Connected through hyperedge HE-1 (induced by source paper "spams meet cryptocurrencies sextortion in the bitcoin ecosystem"):
  with members:
    - "ransomware payments in the bitcoin ecosystem"
    - "blocksci design and applications of a blockchain analysis platform"
    - "economic analysis of ransomware"
    - "the unreasonable effectiveness of address clustering"

Connected through hyperedge HE-2 (induced by source paper "development of email classifier in brazilian portuguese using feature selection for automatic response"):
  with members:
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-3 (induced by source paper "finding subcube heavy hitters in analytics data streams"):
  with members:
    - "testing ising models"
    - "graphical model sketch"
    - "space lower bounds for itemset frequency sketches"
    - "measuring independence of datasets"

Connected through hyperedge HE-4 (induced by source paper "stacking classifiers for anti spam filtering of e mail"):
  with members:
    - "machine learning in automated text categorization"
    - "an evaluation of naive bayesian anti spam filtering"
    - "an experimental comparison of naive bayesian and keyword based anti spam filtering with personal e mail messages"

Connected through hyperedge HE-5 (induced by source paper "boosting trees for anti spam email filtering"):
  with members:
    - "an experimental comparison of naive bayesian and keyword based anti spam filtering with personal e mail messages"

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.CL(Computation and Language)