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: "mitre at semeval 2016 task 6 transfer learning for stance detection"
  Abstract: We describe MITRE's submission to the SemEval-2016 Task 6, Detecting Stance in Tweets. This effort achieved the top score in Task A on supervised stance detection, producing an average F1 score of 67.8 when assessing whether a tweet author was in favor or against a topic. We employed a recurrent neural network initialized with features learned via distant supervision on two large unlabeled datasets. We trained embeddings of words and phrases with the word2vec skip-gram method, then used those features to learn sentence representations via a hashtag prediction auxiliary task. These sentence vectors were then fine-tuned for stance detection on several hundred labeled examples. The result was a high performing system that used transfer learning to maximize the value of the available training data.

Connected through hyperedge HE-1 (induced by source paper "dan dual view representation learning for adapting stance classifiers to new domains"):
  with members:
    - "recognising agreement and disagreement between stances with reason comparing networks"
    - "domain adaptation with adversarial training and graph embeddings"
    - "aspect augmented adversarial networks for domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "deep memory networks for attitude identification"
    - "stance detection with bidirectional conditional encoding"
    - "deep coral correlation alignment for deep domain adaptation"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-2 (induced by source paper "contrastive language adaptation for cross lingual stance detection"):
  with members:
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "adversarial classifier for imbalanced problems"
    - "all in one multi task learning for rumour verification"
    - "automatic stance detection using end to end memory networks"
    - "deep contextualized word representations"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fact checking in community forums"

Connected through hyperedge HE-3 (induced by source paper "your stance is exposed analysing possible factors for stance detection on social media"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "assessing sentiment of the expressed stance on social media"
    - "automatic stance detection using end to end memory networks"
    - "topical stance detection for twitter a two phase lstm model using attention"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "hiding individuals and communities in a social network"
    - "friends and enemies of clinton and trump using context for detecting stance in political tweets"
    - "reducing controversy by connecting opposing views"

Connected through hyperedge HE-4 (induced by source paper "the evolution of argumentation mining from models to social media and emerging tools"):
  with members:
    - "claudette an automated detector of potentially unfair clauses in online terms of service"
    - "argumentation mining exploiting multiple sources and background knowledge"
    - "multi task learning for argumentation mining in low resource settings"
    - "cross topic argument mining from heterogeneous sources using attention based neural networks"
    - "towards automated factchecking developing an annotation schema and benchmark for consistent automated claim detection"
    - "argumentation mining in user generated web discourse"
    - "the argument reasoning comprehension task identification and reconstruction of implicit warrants"
    - "stance and sentiment in tweets"

Connected through hyperedge HE-5 (induced by source paper "characterizing long running political phenomena on social media"):
  with members:
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"
    - "stance and sentiment in tweets"
    - "identifying users with opposing opinions in twitter debates"

Connected through hyperedge HE-6 (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"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"

Connected through hyperedge HE-7 (induced by source paper "investigating rumor news using agreement aware search"):
  with members:
    - "empower sequence labeling with task aware neural language model"
    - "xgboost a scalable tree boosting system"
    - "stance and sentiment in tweets"
    - "tensorflow a system for large scale machine learning"
    - "learning natural language inference with lstm"
    - "reasoning about entailment with neural attention"
    - "a parallel and efficient algorithm for learning to match"
    - "a survey of paraphrasing and textual entailment methods"

Connected through hyperedge HE-8 (induced by source paper "end to end argument mining for discussion threads based on parallel constrained pointer architecture"):
  with members:
    - "neural end to end learning for computational argumentation mining"
    - "parsing argumentation structures in persuasive essays"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "pointer sentinel mixture models"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

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.AI(Artificial Intelligence)