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: "tweet2vec character based distributed representations for social media"
  Abstract: Text from social media provides a set of challenges that can cause traditional NLP approaches to fail. Informal language, spelling errors, abbreviations, and special characters are all commonplace in these posts, leading to a prohibitively large vocabulary size for word-level approaches. We propose a character composition model, tweet2vec, which finds vector-space representations of whole tweets by learning complex, non-local dependencies in character sequences. The proposed model outperforms a word-level baseline at predicting user-annotated hashtags associated with the posts, doing significantly better when the input contains many out-of-vocabulary words or unusual character sequences. Our tweet2vec encoder is publicly available.

Connected through hyperedge HE-1 (induced by source paper "maskdga a black box evasion technique against dga classifiers and adversarial defenses"):
  with members:
    - "detecting dga domains with recurrent neural networks and side information"
    - "inline detection of domain generation algorithms with context sensitive word embeddings"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "exploring adversarial examples in malware detection"
    - "detection of malicious and low throughput data exfiltration over the dns protocol"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "hotflip white box adversarial examples for text classification"
    - "generating adversarial malware examples for black box attacks based on gan"

Connected through hyperedge HE-2 (induced by source paper "learning invariant representations of social media users"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "deep learning is robust to massive label noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"

Connected through hyperedge HE-3 (induced by source paper "investigating an effective character level embedding in korean sentence classification"):
  with members:
    - "speech intention understanding in a head final language a disambiguation utilizing intonation dependency"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "real time automatic word segmentation for user generated text"
    - "attention is all you need"
    - "a sub character architecture for korean language processing"
    - "which encoding is the best for text classification in chinese english japanese and korean"
    - "a structured self attentive sentence embedding"
    - "neural language correction with character based attention"

Connected through hyperedge HE-4 (induced by source paper "deep representation learning for clustering of health tweets"):
  with members:
    - "characterizing transgender health issues in twitter"
    - "improving topic models with latent feature word representations"
    - "clustering and unsupervised anomaly detection with l2 normalized deep auto encoder representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character based neural embeddings for tweet clustering"
    - "multi task learning for mental health using social media text"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-5 (induced by source paper "how to evaluate word representations of informal domain"):
  with members:
    - "adaptive region embedding for text classification"
    - "emo love and god making sense of urban dictionary a crowd sourced online dictionary"
    - "learning to explain non standard english words and phrases"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "evaluating informal domain word representations with urbandictionary"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-6 (induced by source paper "mapping dis information flow about the mh17 plane crash"):
  with members:
    - "universal dependency parsing from scratch"
    - "issue framing in online discussion fora"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "junk news on military affairs and national security social media disinformation campaigns against us military personnel and veterans"
    - "framing and agenda setting in russian news a computational analysis of intricate political strategies"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "neural discourse structure for text categorization"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-7 (induced by source paper "multi task pairwise neural ranking for hashtag segmentation"):
  with members:
    - "microblog hashtag generation via encoding conversation contexts"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "exploring the limits of weakly supervised pretraining"
    - "emo love and god making sense of urban dictionary a crowd sourced online dictionary"
    - "towards deep semantic analysis of hashtags"
    - "adam a method for stochastic optimization"
    - "empirical methods for compound splitting"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-8 (induced by source paper "towards successful social media advertising predicting the influence of commercial tweets"):
  with members:
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"
    - "klout score measuring influence across multiple social networks"
    - "who will retweet this automatically identifying and engaging strangers on twitter to spread information"
    - "distributed representations of sentences and documents"
    - "can cascades be predicted"
    - "efficient estimation of word representations in vector space"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"
    - "twitter mood predicts the stock market"

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.LG(Machine Learning)