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: "harnessing cognitive features for sarcasm detection"
  Abstract: In this paper, we propose a novel mechanism for enriching the feature vector, for the task of sarcasm detection, with cognitive features extracted from eye-movement patterns of human readers. Sarcasm detection has been a challenging research problem, and its importance for NLP applications such as review summarization, dialog systems and sentiment analysis is well recognized. Sarcasm can often be traced to incongruity that becomes apparent as the full sentence unfolds. This presence of incongruity- implicit or explicit- affects the way readers eyes move through the text. We observe the difference in the behaviour of the eye, while reading sarcastic and non sarcastic sentences. Motivated by his observation, we augment traditional linguistic and stylistic features for sarcasm detection with the cognitive features obtained from readers eye movement data. We perform statistical classification using the enhanced feature set so obtained. The augmented cognitive features improve sarcasm detection by 3.7% (in terms of F-score), over the performance of the best reported system.

Connected through hyperedge HE-1 (induced by source paper "interpreting verbal irony linguistic strategies and the connection to thetype of semantic incongruity"):
  with members:
    - "exploring author context for detecting intended vs perceived sarcasm"
    - "towards multimodal sarcasm detection an _obviously_ perfect paper"
    - "sarcasm analysis using conversation context"
    - "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "the role of conversation context for sarcasm detection in online interactions"
    - "modelling context with user embeddings for sarcasm detection in social media"

Connected through hyperedge HE-2 (induced by source paper "towards multimodal sarcasm detection an _obviously_ perfect paper"):
  with members:
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "representing social media users for sarcasm detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "tensor fusion network for multimodal sentiment analysis"

Connected through hyperedge HE-3 (induced by source paper "zuco 2 0 a dataset of physiological recordings during natural reading and annotation"):
  with members:
    - "interpreting and improving natural language processing in machines with natural language processing in the brain"
    - "cognival a framework for cognitive word embedding evaluation"
    - "finding syntax in human encephalography with beam search"
    - "improving sentence compression by learning to predict gaze"

Connected through hyperedge HE-4 (induced by source paper "sarcasm analysis using conversation context"):
  with members:
    - "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition"
    - "conversation modeling on reddit using a graph structured lstm"
    - "are you serious rhetorical questions and sarcasm in social media dialog"
    - "a large self annotated corpus for sarcasm"
    - "the role of conversation context for sarcasm detection in online interactions"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "are word embedding based features useful for sarcasm detection"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-5 (induced by source paper "automatic sarcasm detection a survey"):
  with members:
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "a large self annotated corpus for sarcasm"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "are word embedding based features useful for sarcasm detection"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "who would have thought of that a hierarchical topic model for extraction of sarcasm prevalent topics and sarcasm detection"
    - "indonesian social media sentiment analysis with sarcasm detection"

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)