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: "modeling ambiguity subjectivity and diverging viewpoints in opinion question answering systems"
  Abstract: Product review websites provide an incredible lens into the wide variety of opinions and experiences of different people, and play a critical role in helping users discover products that match their personal needs and preferences. To help address questions that can't easily be answered by reading others' reviews, some review websites also allow users to pose questions to the community via a question-answering (QA) system. As one would expect, just as opinions diverge among different reviewers, answers to such questions may also be subjective, opinionated, and divergent. This means that answering such questions automatically is quite different from traditional QA tasks, where it is assumed that a single `correct' answer is available. While recent work introduced the idea of question-answering using product reviews, it did not account for two aspects that we consider in this paper: (1) Questions have multiple, often divergent, answers, and this full spectrum of answers should somehow be used to train the system; and (2) What makes a `good' answer depends on the asker and the answerer, and these factors should be incorporated in order for the system to be more personalized. Here we build a new QA dataset with 800 thousand questions---and over 3.1 million answers---and show that explicitly accounting for personalization and ambiguity leads both to quantitatively better answers, but also a more nuanced view of the range of supporting, but subjective, opinions.

Connected through hyperedge HE-1 (induced by source paper "review guided helpful answer identification in e commerce"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "joint learning of answer selection and answer summary generation in community question answering"
    - "learning semantic textual similarity from conversations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-2 (induced by source paper "convert efficient and accurate conversational representations from transformers"):
  with members:
    - "deep retrieval based dialogue systems a short review"
    - "q8bert quantized 8bit bert"
    - "designing for health chatbots"
    - "are sixteen heads really better than one"
    - "dialog system technology challenge 7"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "extreme language model compression with optimal subwords and shared projections"

Connected through hyperedge HE-3 (induced by source paper "training neural response selection for task oriented dialogue systems"):
  with members:
    - "passage re ranking with bert"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "simple applications of bert for ad hoc document retrieval"
    - "a repository of conversational datasets"
    - "multi task deep neural networks for natural language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "learning semantic textual similarity from conversations"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"

Connected through hyperedge HE-4 (induced by source paper "a repository of conversational datasets"):
  with members:
    - "designing for health chatbots"
    - "training neural response selection for task oriented dialogue systems"
    - "learning semantic textual similarity from conversations"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "fully statistical neural belief tracking"
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "deep contextualized word representations"
    - "universal sentence encoder"

Connected through hyperedge HE-5 (induced by source paper "time to take emoji seriously they vastly improve casual conversational models"):
  with members:
    - "a repository of conversational datasets"
    - "ana at semeval 2019 task 3 contextual emotion detection in conversations through hierarchical lstms and bert"
    - "training millions of personalized dialogue agents"
    - "a large scale corpus for conversation disentanglement"
    - "coqa a conversational question answering challenge"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "self representation on twitter using emoji skin color modifiers"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "open information extraction from question answer pairs"):
  with members:
    - "transforming question answering datasets into natural language inference datasets"
    - "neural open information extraction"
    - "opennmt open source toolkit for neural machine translation"
    - "indirect supervision for relation extraction using question answer pairs"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "learning a neural semantic parser from user feedback"
    - "a syntactic neural model for general purpose code generation"
    - "a constrained sequence to sequence neural model for sentence simplification"

Connected through hyperedge HE-7 (induced by source paper "why does a visual question have different answers"):
  with members:
    - "vizwiz grand challenge answering visual questions from blind people"
    - "crowdsourcing ground truth for medical relation extraction"
    - "an analysis of visual question answering algorithms"
    - "learning to disambiguate by asking discriminative questions"
    - "the promise of premise harnessing question premises in visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "crowdsourcing question answer meaning representations"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-8 (induced by source paper "review based question generation with adaptive instance transfer and augmentation"):
  with members:
    - "answerer in questioner s mind information theoretic approach to goal oriented visual dialog"
    - "dual ask answer network for machine reading comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "difficulty controllable generation of reading comprehension questions"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "learning to ask neural question generation for reading comprehension"
    - "two stage synthesis networks for transfer learning in machine comprehension"

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.IR(Information Retrieval)