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: "kashmir a computational analysis of the voice of peace"
  Abstract: The recent Pulwama terror attack (February 14, 2019, Pulwama, Kashmir) triggered a chain of escalating events between India and Pakistan adding another episode to their 70-year-old dispute over Kashmir. The present era of ubiquitious social media has never seen nuclear powers closer to war. In this paper, we analyze this evolving international crisis via a substantial corpus constructed using comments on YouTube videos (921,235 English comments posted by 392,460 users out of 2.04 million overall comments by 791,289 users on 2,890 videos). Our main contributions in the paper are three-fold. First, we present an observation that polyglot word-embeddings reveal precise and accurate language clusters, and subsequently construct a document language-identification technique with negligible annotation requirements. We demonstrate the viability and utility across a variety of data sets involving several low-resource languages. Second, we present an extensive analysis on temporal trends of pro-peace and pro-war intent through a manually constructed polarity phrase lexicon. We observe that when tensions between the two nations were at their peak, pro-peace intent in the corpus was at its highest point. Finally, in the context of heated discussions in a politically tense situation where two nations are at the brink of a full-fledged war, we argue the importance of automatic identification of user-generated web content that can diffuse hostility and address this prediction task, dubbed \emph{hope-speech detection}.

Connected through hyperedge HE-1 (induced by source paper "social media attributions in the context of water crisis"):
  with members:
    - "who blames whom in a crisis detecting blame ties from news articles using neural networks"
    - "voice for the voiceless active sampling to detect comments supporting the rohingyas"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "huggingface s transformers state of the art natural language processing"
    - "analyzing polarization in social media method and application to tweets on 21 mass shootings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attribution of responsibility and blame regarding a man made disaster flintwatercrisis"

Connected through hyperedge HE-2 (induced by source paper "hope speech detection a computational analysis of the voice of peace"):
  with members:
    - "something s brewing early prediction of controversy causing posts from discussion features"
    - "polyglot contextual representations improve crosslingual transfer"
    - "low resource parsing with crosslingual contextualized representations"
    - "polyglot semantic role labeling"
    - "forecasting the presence and intensity of hostility on instagram using linguistic and social features"
    - "mind your language abuse and offense detection for code switched languages"
    - "learning to generate reviews and discovering sentiment"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-3 (induced by source paper "voice for the voiceless active sampling to detect comments supporting the rohingyas"):
  with members:
    - "sentiment analysis of comments on rohingya movement with support vector machine"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "online social media in the syria conflict encompassing the extremes and the in betweens"

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.CY(Computers and Society)