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: "characterizing linguistic attributes for automatic classification of intent based racist radicalized posts on tumblr micro blogging website"
  Abstract: Research shows that many like-minded people use popular microblogging websites for posting hateful speech against various religions and race. Automatic identification of racist and hate promoting posts is required for building social media intelligence and security informatics based solutions. However, just keyword spotting based techniques cannot be used to accurately identify the intent of a post. In this paper, we address the challenge of the presence of ambiguity in such posts by identifying the intent of author. We conduct our study on Tumblr microblogging website and develop a cascaded ensemble learning classifier for identifying the posts having racist or radicalized intent. We train our model by identifying various semantic, sentiment and linguistic features from free-form text. Our experimental results shows that the proposed approach is effective and the emotion tone, social tendencies, language cues and personality traits of a narrative are discriminatory features for identifying the racist intent behind a post.

Connected through hyperedge HE-1 (induced by source paper "developing a multilingual annotated corpus of misogyny and aggression"):
  with members:
    - "when a tweet is actually sexist a more comprehensive classification of different online harassment categories and the challenges in nlp"
    - "predicting the type and target of offensive posts in social media"
    - "challenges in discriminating profanity from hate speech"
    - "aggression annotated corpus of hindi english code mixed data"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "automated hate speech detection and the problem of offensive language"
    - "detecting hate speech in social media"
    - "modeling trolling in social media conversations"

Connected through hyperedge HE-2 (induced by source paper "investigating classification techniques with feature selection for intention mining from twitter feed"):
  with members:
    - "feature selection a data perspective"
    - "emotion intensities in tweets"
    - "the lumiere project bayesian user modeling for inferring the goals and needs of software users"

Connected through hyperedge HE-3 (induced by source paper "towards open intent discovery for conversational text"):
  with members:
    - "joint slot filling and intent detection via capsule neural networks"
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "zero shot user intent detection via capsule neural networks"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "multi domain adversarial learning for slot filling in spoken language understanding"

Connected through hyperedge HE-4 (induced by source paper "seminar users in the arabic twitter sphere"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "an army of me sockpuppets in online discussion communities"
    - "online human bot interactions detection estimation and characterization"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "botornot a system to evaluate social bots"
    - "dissecting a social botnet growth content and influence in twitter"
    - "stance classification in rumours as a sequential task exploiting the tree structure of social media conversations"

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)