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: "an experimental comparison of naive bayesian and keyword based anti spam filtering with personal e mail messages"
  Abstract: The growing problem of unsolicited bulk e-mail, also known as "spam", has generated a need for reliable anti-spam e-mail filters. Filters of this type have so far been based mostly on manually constructed keyword patterns. An alternative approach has recently been proposed, whereby a Naive Bayesian classifier is trained automatically to detect spam messages. We test this approach on a large collection of personal e-mail messages, which we make publicly available in "encrypted" form contributing towards standard benchmarks. We introduce appropriate cost-sensitive measures, investigating at the same time the effect of attribute-set size, training-corpus size, lemmatization, and stop lists, issues that have not been explored in previous experiments. Finally, the Naive Bayesian filter is compared, in terms of performance, to a filter that uses keyword patterns, and which is part of a widely used e-mail reader.

Connected through hyperedge HE-1 (induced by source paper "the history of digital spam"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "the darpa twitter bot challenge"
    - "the rise of social bots"
    - "manipulation and abuse on social media by emilio ferrara with ching man au yeung as coordinator"
    - "on facebook most ties are weak"
    - "uncovering social network sybils in the wild"

Connected through hyperedge HE-2 (induced by source paper "an automated text categorization framework based on hyperparameter optimization"):
  with members:
    - "universal unsupervised rule based uncovered sentiment analysis"
    - "n gram new groningen author profiling model"
    - "multilingual twitter sentiment classification the role of human annotators"
    - "distributed representations of words and phrases and their compositionality"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-3 (induced by source paper "identifying retweetable tweets with a personalized global classifier"):
  with members:
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "adaptive spam detection inspired by a cross regulation model of immune dynamics a study of concept drift"):
  with members:
    - "uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks"

Connected through hyperedge HE-5 (induced by source paper "in vivo spam filtering a challenge problem for data mining"):
  with members:
    - "an evaluation of naive bayesian anti spam filtering"
    - "robust classification for imprecise environments"

Connected through hyperedge HE-6 (induced by source paper "machine learning in automated text categorization"):
  with members:
    - "boosting applied to word sense disambiguation"
    - "learning to resolve natural language ambiguities a unified approach"

Connected through hyperedge HE-7 (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"
    - "learning to filter spam e mail a comparison of a naive bayesian and a memory based approach"

Connected through hyperedge HE-8 (induced by source paper "boosting trees for anti spam email filtering"):
  with members:
    - "learning to filter spam e mail a comparison of a naive bayesian and a memory based approach"

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)