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: "spider and the flies focused crawling on tumblr to detect hate promoting communities"
  Abstract: Tumblr is one of the largest and most popular microblogging website on the Internet. Studies shows that due to high reachability among viewers, low publication barriers and social networking connectivity, microblogging websites are being misused as a platform to post hateful speech and recruiting new members by existing extremist groups. Manual identification of such posts and communities is overwhelmingly impractical due to large amount of posts and blogs being published every day. We propose a topic based web crawler primarily consisting of multiple phases: training a text classifier model consisting examples of only hate promoting users, extracting posts of an unknown tumblr micro-blogger, classifying hate promoting bloggers based on their activity feeds, crawling through the external links to other bloggers and performing a social network analysis on connected extremist bloggers. To investigate the effectiveness of our approach, we conduct experiments on large real world dataset. Experimental results reveals that the proposed approach is an effective method and has an F-score of 0.80. We apply social network analysis based techniques and identify influential and core bloggers in a community.

Connected through hyperedge HE-1 (induced by source paper "modeling islamist extremist communications on social media using contextual dimensions religion ideology and hate"):
  with members:
    - "predictive analysis on twitter techniques and applications"
    - "the rise of jihadist propaganda on social networks"
    - "domain adapted word embeddings for improved sentiment classification"
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "probabilistic fasttext for multi sense word embeddings"
    - "automated hate speech detection and the problem of offensive language"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-2 (induced by source paper "algorithmic extremism examining youtube s rabbit hole of radicalization"):
  with members:
    - "auditing radicalization pathways on youtube"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "sentiment analysis on youtube a brief survey"
    - "force directed graph drawing using social gravity and scaling"

Connected through hyperedge HE-3 (induced by source paper "contagion dynamics of extremist propaganda in social networks"):
  with members:
    - "the rise of jihadist propaganda on social networks"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "online human bot interactions detection estimation and characterization"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "the darpa twitter bot challenge"
    - "network structure and resilience of mafia syndicates"
    - "the rise of social bots"
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"

Connected through hyperedge HE-4 (induced by source paper "predicting online extremism content adopters and interaction reciprocity"):
  with members:
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "the rise of social bots"
    - "failedrevolutions using twitter to study the antecedents of isis support"
    - "applying social media intelligence for predicting and identifying on line radicalization and civil unrest oriented threats"
    - "evolution of online user behavior during a social upheaval"
    - "the digital evolution of occupy wall street"
    - "the geospatial characteristics of a social movement communication network"

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)