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: "network analysis of recurring youtube spam campaigns"
  Abstract: As the popularity of content sharing websites such as YouTube and Flickr has increased, they have become targets for spam, phishing and the distribution of malware. On YouTube, the facility for users to post comments can be used by spam campaigns to direct unsuspecting users to bogus e-commerce websites. In this paper, we demonstrate how such campaigns can be tracked over time using network motif profiling, i.e. by tracking counts of indicative network motifs. By considering all motifs of up to five nodes, we identify discriminating motifs that reveal two distinctly different spam campaign strategies. One of these strategies uses a small number of spam user accounts to comment on a large number of videos, whereas a larger number of accounts is used with the other. We present an evaluation that uses motif profiling to track two active campaigns matching these strategies, and identify some of the associated user accounts.

Connected through hyperedge HE-1 (induced by source paper "natural language processing sentiment analysis and clinical analytics"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"

Connected through hyperedge HE-2 (induced by source paper "eggs a flexible approach to relational modeling of social network spam"):
  with members:
    - "detecting smart spammers on social network a topic model approach"
    - "the libra toolkit for probabilistic models"
    - "hinge loss markov random fields and probabilistic soft logic"

Connected through hyperedge HE-3 (induced by source paper "disturbed youtube for kids characterizing and detecting inappropriate videos targeting young children"):
  with members:
    - "combating the elsagate phenomenon deep learning architectures for disturbing cartoons"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "characterizing videos audience and advertising in youtube channels for kids"
    - "kidstube detection characterization and analysis of child unsafe content promoters on youtube"
    - "tensorflow a system for large scale machine learning"
    - "going deeper with convolutions"
    - "mining user comment activity for detecting forum spammers in youtube"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-4 (induced by source paper "understanding the signature of controversial wikipedia articles through motifs in editor revision networks"):
  with members:
    - "network classification in temporal networks using motifs"
    - "collective attention in the age of mis information"

Connected through hyperedge HE-5 (induced by source paper "big data and social medical sciences state of the art and future trends"):
  with members:
    - "gender identity and lexical variation in social media"

Connected through hyperedge HE-6 (induced by source paper "in a world that counts clustering and detecting fake social engagement at scale"):
  with members:
    - "overlapping community detection via local spectral clustering"
    - "uncovering the small community structure in large networks a local spectral approach"
    - "heat kernel based community detection"
    - "paying for likes understanding facebook like fraud using honeypots"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "semi supervised eigenvectors for large scale locally biased learning"
    - "finding endogenously formed communities"
    - "demon a local first discovery method for overlapping communities"

Connected through hyperedge HE-7 (induced by source paper "subgraph counting color coding beyond trees"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "why do simple algorithms for triangle enumeration work in the real world"
    - "identifying discriminating network motifs in youtube spam"

Connected through hyperedge HE-8 (induced by source paper "follow spam detection based on cascaded social information"):
  with members:
    - "towards detecting compromised accounts on social networks"
    - "the social world of content abusers in community question answering"
    - "sybilbelief a semi supervised learning approach for structure based sybil detection"

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.SI(Social and Information Networks)