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: "creating a live public short message service corpus the nus sms corpus"
  Abstract: Short Message Service (SMS) messages are short messages sent from one person to another from their mobile phones. They represent a means of personal communication that is an important communicative artifact in our current digital era. As most existing studies have used private access to SMS corpora, comparative studies using the same raw SMS data have not been possible up to now. We describe our efforts to collect a public SMS corpus to address this problem. We use a battery of methodologies to collect the corpus, paying particular attention to privacy issues to address contributors’ concerns. Our live project collects new SMS message submissions, checks their quality, and adds valid messages. We release the resultant corpus as XML and as SQL dumps, along with monthly corpus statistics. We opportunistically collect as much metadata about the messages and their senders as possible, so as to enable different types of analyses. To date, we have collected more than 71,000 messages, focusing on English and Mandarin Chinese.

Connected through hyperedge HE-1 (induced by source paper "affinity a system for latent user similarity comparison on texting data"):
  with members:
    - "tydr track your daily routine android app for tracking smartphone sensor and usage data"
    - "context data categories and privacy model for mobile data collection apps"
    - "do you like what i like similarity estimation in proximity based mobile social networks"
    - "towards psychometrics based friend recommendations in social networking services"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "enriching word vectors with subword information"
    - "efficient estimation of word representations in vector space"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-2 (induced by source paper "semeval 2014 task 9 sentiment analysis in twitter"):
  with members:
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-3 (induced by source paper "a dataset for building code mixed goal oriented conversation systems"):
  with members:
    - "part of speech tagging for code switched data"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "a persona based neural conversation model"
    - "learning end to end goal oriented dialog"
    - "query reduction networks for question answering"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-4 (induced by source paper "a survey of available corpora for building data driven dialogue systems"):
  with members:
    - "a persona based neural conversation model"
    - "tweet2vec character based distributed representations for social media"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "end to end lstm based dialog control optimized with supervised and reinforcement learning"
    - "dialog based language learning"
    - "ms marco a human generated machine reading comprehension dataset"

Connected through hyperedge HE-5 (induced by source paper "on detecting messaging abuse in short text messages using linguistic and behavioral patterns"):
  with members:
    - "network analysis of recurring youtube spam campaigns"
    - "machine learning in automated text categorization"

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)