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: "best worst scaling more reliable than rating scales a case study on sentiment intensity annotation"
  Abstract: Rating scales are a widely used method for data annotation; however, they present several challenges, such as difficulty in maintaining inter- and intra-annotator consistency. Best-worst scaling (BWS) is an alternative method of annotation that is claimed to produce high-quality annotations while keeping the required number of annotations similar to that of rating scales. However, the veracity of this claim has never been systematically established. Here for the first time, we set up an experiment that directly compares the rating scale method with BWS. We show that with the same total number of annotations, BWS produces significantly more reliable results than the rating scale.

Connected through hyperedge HE-1 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents"):
  with members:
    - "evaluating coherence in dialogue systems using entailment"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "rankme reliable human ratings for natural language generation"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "augmenting neural response generation with context aware topical attention"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-2 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks"):
  with members:
    - "fine tune bert for extractive summarization"
    - "jointly extracting and compressing documents with summary state representations"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "mass masked sequence to sequence pre training for language generation"
    - "generating summaries with topic templates and structured convolutional decoders"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "summary level training of sentence rewriting for abstractive summarization"):
  with members:
    - "fine tune bert for extractive summarization"
    - "efficient adaptation of pretrained transformers for abstractive summarization"
    - "bertscore evaluating text generation with bert"
    - "pretraining based natural language generation for text summarization"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "neural extractive text summarization with syntactic compression"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-4 (induced by source paper "text summarization with pretrained encoders"):
  with members:
    - "pre trained language model representations for language generation"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "unified language model pre training for natural language understanding and generation"
    - "leveraging pre trained checkpoints for sequence generation tasks"
    - "deep communicating agents for abstractive summarization"
    - "bottom up abstractive summarization"
    - "banditsum extractive summarization as a contextual bandit"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "hierarchical transformers for multi document summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "structured neural summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "generating wikipedia by summarizing long sequences"
    - "towards dynamic computation graphs via sparse latent structure"
    - "bottom up abstractive summarization"
    - "automatic detection of vague words and sentences in privacy policies"

Connected through hyperedge HE-6 (induced by source paper "multi news a large scale multi document summarization dataset and abstractive hierarchical model"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bottom up abstractive summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "adapting the neural encoder decoder framework from single to multi document summarization"

Connected through hyperedge HE-7 (induced by source paper "informative and controllable opinion summarization"):
  with members:
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "hierarchical transformers for multi document summarization"
    - "generating summaries with topic templates and structured convolutional decoders"
    - "improving abstraction in text summarization"
    - "generating wikipedia by summarizing long sequences"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "controlling linguistic style aspects in neural language generation"

Connected through hyperedge HE-8 (induced by source paper "towards best experiment design for evaluating dialogue system output"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "evaluating coherence in dialogue systems using entailment"
    - "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"
    - "rankme reliable human ratings for natural language generation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "augmenting neural response generation with context aware topical attention"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "hierarchical recurrent attention network for response generation"

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)