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: "multi task learning for argumentation mining in low resource settings"
  Abstract: We investigate whether and where multi-task learning (MTL) can improve performance on NLP problems related to argumentation mining (AM), in particular argument component identification. Our results show that MTL performs particularly well (and better than single-task learning) when little training data is available for the main task, a common scenario in AM. Our findings challenge previous assumptions that conceptualizations across AM datasets are divergent and that MTL is difficult for semantic or higher-level tasks.

Connected through hyperedge HE-1 (induced by source paper "stance detection benchmark how robust is your stance detection"):
  with members:
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "stancy stance classification based on consistency cues"
    - "multi task deep neural networks for natural language understanding"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "combating adversarial misspellings with robust word recognition"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "sampling bias in deep active classification an empirical study"

Connected through hyperedge HE-2 (induced by source paper "po emo conceptualization annotation and modeling of aesthetic emotions in german and english poetry"):
  with members:
    - "towards multimodal emotion recognition in german speech events in cars using transfer learning"
    - "dens a dataset for multi class emotion analysis"
    - "semantic change and emerging tropes in a large corpus of new high german poetry"
    - "diachronic topics in new high german poetry"
    - "iest wassa 2018 implicit emotions shared task"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "gated feedback recurrent neural networks"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-3 (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"
    - "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"
    - "mitre at semeval 2016 task 6 transfer learning for stance detection"
    - "stance and sentiment in tweets"

Connected through hyperedge HE-4 (induced by source paper "neural symbolic argumentation mining an argument in favour of deep learning and reasoning"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "argument component classification for classroom discussions"
    - "what is the essence of a claim cross domain claim identification"
    - "deep residual learning for weakly supervised relation extraction"
    - "argument mining with structured svms and rnns"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "defeasible logic programming an argumentative approach"

Connected through hyperedge HE-5 (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"
    - "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"
    - "modelling context with user embeddings for sarcasm detection in social media"

Connected through hyperedge HE-6 (induced by source paper "cross lingual argumentation mining machine translation and a bit of projection is all you need"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "neural end to end learning for computational argumentation mining"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "crowdsourcing argumentation structures in chinese hotel reviews"
    - "cross lingual character level neural morphological tagging"
    - "cross lingual and cross domain discourse segmentation of entire documents"
    - "cross lingual models of word embeddings an empirical comparison"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-7 (induced by source paper "challenges in the automatic analysis of students diagnostic reasoning"):
  with members:
    - "learning word vectors for 157 languages"
    - "argumentation mining in user generated web discourse"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "condensed memory networks for clinical diagnostic inferencing"
    - "learning recursive segments for discourse parsing"

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)