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: "understanding task design trade offs in crowdsourced paraphrase collection"
  Abstract: Linguistically diverse datasets are critical for training and evaluating robust machine learning systems, but data collection is a costly process that often requires experts. Crowdsourcing the process of paraphrase generation is an effective means of expanding natural language datasets, but there has been limited analysis of the trade-offs that arise when designing tasks. In this paper, we present the first systematic study of the key factors in crowdsourcing paraphrase collection. We consider variations in instructions, incentives, data domains, and workflows. We manually analyzed paraphrases for correctness, grammaticality, and linguistic diversity. Our observations provide new insight into the trade-offs between accuracy and diversity in crowd responses that arise as a result of task design, providing guidance for future paraphrase generation procedures.

Connected through hyperedge HE-1 (induced by source paper "outlier detection for improved data quality and diversity in dialog systems"):
  with members:
    - "deep contextualized word representations"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"

Connected through hyperedge HE-2 (induced by source paper "challenge ai mind a crowd system for proactive ai testing"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "towards crafting text adversarial samples"
    - "generating natural adversarial examples"
    - "crafting adversarial input sequences for recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "improving text to sql evaluation methodology"):
  with members:
    - "learning a neural semantic parser from user feedback"
    - "language to logical form with neural attention"
    - "learning a natural language interface with neural programmer"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"
    - "learning dependency based compositional semantics"

Connected through hyperedge HE-4 (induced by source paper "crowdsourcing for reminiscence chatbot design"):
  with members:
    - "smart conversational agents for reminiscence"
    - "is there anything else i can help you with challenges in deploying an on demand crowd powered conversational agent"
    - "designing for older adults review of touchscreen design guidelines"

Connected through hyperedge HE-5 (induced by source paper "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "learning to generate reviews and discovering sentiment"
    - "a continuously growing dataset of sentential paraphrases"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "learning paraphrastic sentence embeddings from back translated bitext"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"

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)