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 learning tweet embeddings using character level cnn lstm encoder decoder"
  Abstract: We present Tweet2Vec, a novel method for generating general-purpose vector representation of tweets. The model learns tweet embeddings using character-level CNN-LSTM encoder-decoder. We trained our model on 3 million, randomly selected English-language tweets. The model was evaluated using two methods: tweet semantic similarity and tweet sentiment categorization, outperforming the previous state-of-the-art in both tasks. The evaluations demonstrate the power of the tweet embeddings generated by our model for various tweet categorization tasks. The vector representations generated by our model are generic, and hence can be applied to a variety of tasks. Though the model presented in this paper is trained on English-language tweets, the method presented can be used to learn tweet embeddings for different languages.

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 "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 character based distributed representations for social media"
    - "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-4 (induced by source paper "neural language model based training data augmentation for weakly supervised early rumor detection"):
  with members:
    - "all in one multi task learning for rumour verification"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "deep contextualized word representations"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "a longitudinal assessment of the persistence of twitter datasets"
    - "deepstance at semeval 2016 task 6 detecting stance in tweets using character and word level cnns"
    - "learning reporting dynamics during breaking news for rumour detection in social media"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-5 (induced by source paper "towards successful social media advertising predicting the influence of commercial tweets"):
  with members:
    - "tweet2vec character based distributed representations for social media"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "tweets can tell activity recognition using hybrid long short term memory model"):
  with members:
    - "lstm a search space odyssey"
    - "attention is all you need"
    - "tweet2vec character based distributed representations for social media"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "learning natural language inference using bidirectional lstm model and inner attention"
    - "modeling rich contexts for sentiment classification with lstm"
    - "a c lstm neural network for text classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-7 (induced by source paper "learning representations of social media users"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "multi task learning for argumentation mining in low resource settings"
    - "semi supervised user geolocation via graph convolutional networks"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "deep generalized canonical correlation analysis"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "modelling context with user embeddings for sarcasm detection in social media"

Connected through hyperedge HE-8 (induced by source paper "crnn a joint neural network for redundancy detection"):
  with members:
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "multichannel variable size convolution for sentence classification"
    - "minimal gated unit for recurrent neural networks"
    - "mgnc cnn a simple approach to exploiting multiple word embeddings for sentence classification"
    - "character level convolutional networks for text classification"
    - "skip thought vectors"
    - "character aware neural language models"
    - "gated feedback recurrent neural networks"

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.CL(Computation and Language)