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: "making the most of tweet inherent features for social spam detection on twitter"
  Abstract: Social spam produces a great amount of noise on social media services such as Twitter, which reduces the signal-to-noise ratio that both end users and data mining applications observe. Existing techniques on social spam detection have focused primarily on the identification of spam accounts by using extensive historical and network-based data. In this paper we focus on the detection of spam tweets, which optimises the amount of data that needs to be gathered by relying only on tweet-inherent features. This enables the application of the spam detection system to a large set of tweets in a timely fashion, potentially applicable in a real-time or near real-time setting. Using two large hand-labelled datasets of tweets containing spam, we study the suitability of five classification algorithms and four different feature sets to the social spam detection task. Our results show that, by using the limited set of features readily available in a tweet, we can achieve encouraging results which are competitive when compared against existing spammer detection systems that make use of additional, costly user features. Our study is the first that attempts at generalising conclusions on the optimal classifiers and sets of features for social spam detection over different datasets.

Connected through hyperedge HE-1 (induced by source paper "real time detection of content polluters in partially observable twitter networks"):
  with members:
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "privacy and security in online social networks a survey"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "the rise of social bots"
    - "who will retweet this automatically identifying and engaging strangers on twitter to spread information"

Connected through hyperedge HE-2 (induced by source paper "large scale crowdsourcing and characterization of twitter abusive behavior"):
  with members:
    - "like sheep among wolves characterizing hateful users on twitter"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "detecting hate speech in social media"
    - "deep learning for hate speech detection in tweets"
    - "one step and two step classification for abusive language detection on twitter"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "the social world of content abusers in community question answering"

Connected through hyperedge HE-3 (induced by source paper "on the fly detection of autogenerated tweets"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "the darpa twitter bot challenge"
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"
    - "where in the world are you geolocation and language identification in twitter"
    - "estimating continuous distributions in bayesian classifiers"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-4 (induced by source paper "semi supervised spam detection in twitter stream"):
  with members:
    - "the rise of social bots"

Connected through hyperedge HE-5 (induced by source paper "detecting smart spammers on social network a topic model approach"):
  with members:
    - "the rise of social bots"
    - "scikit learn machine learning in python"

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.IR(Information Retrieval)