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: "enhanced twitter sentiment classification using contextual information"
  Abstract: The rise in popularity and ubiquity of Twitter has made sentiment analysis of tweets an important and well-covered area of research. However, the 140 character limit imposed on tweets makes it hard to use standard linguistic methods for sentiment classification. On the other hand, what tweets lack in structure they make up with sheer volume and rich metadata. This metadata includes geolocation, temporal and author information. We hypothesize that sentiment is dependent on all these contextual factors. Different locations, times and authors have different emotional valences. In this paper, we explored this hypothesis by utilizing distant supervision to collect millions of labelled tweets from different locations, times and authors. We used this data to analyse the variation of tweet sentiments across different authors, times and locations. Once we explored and understood the relationship between these variables and sentiment, we used a Bayesian approach to combine these variables with more standard linguistic features such as n-grams to create a Twitter sentiment classifier. This combined classifier outperforms the purely linguistic classifier, showing that integrating the rich contextual information available on Twitter into sentiment classification is a promising direction of research.

Connected through hyperedge HE-1 (induced by source paper "emotion detection with neural personal discrimination"):
  with members:
    - "attention is all you need"
    - "a sentiment and semantics based approach for emotion detection in textual conversations"
    - "long short term memory networks for machine reading"
    - "a primer on neural network models for natural language processing"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-2 (induced by source paper "efficient twitter sentiment classification using subjective distant supervision"):
  with members:
    - "sentiment analysis of twitter data a survey of techniques"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-3 (induced by source paper "a sentiment and semantics based approach for emotion detection in textual conversations"):
  with members:
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "deepstance at semeval 2016 task 6 detecting stance in tweets using character and word level cnns"):
  with members:
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"
    - "text understanding from scratch"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "natural language processing almost from scratch"
    - "get out the vote determining support or opposition from congressional floor debate transcripts"

Connected through hyperedge HE-5 (induced by source paper "digital stylometry linking profiles across social networks"):
  with members:
    - "on the reliability of profile matching across large online social networks"
    - "studying user footprints in different online social 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.SI(Social and Information Networks)