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: "learning semantic textual similarity from conversations"
  Abstract: We present a novel approach to learn representations for sentence-level semantic similarity using conversational data. Our method trains an unsupervised model to predict conversational input-response pairs. The resulting sentence embeddings perform well on the semantic textual similarity (STS) benchmark and SemEval 2017's Community Question Answering (CQA) question similarity subtask. Performance is further improved by introducing multitask training combining the conversational input-response prediction task and a natural language inference task. Extensive experiments show the proposed model achieves the best performance among all neural models on the STS benchmark and is competitive with the state-of-the-art feature engineered and mixed systems in both tasks.

Connected through hyperedge HE-1 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions"):
  with members:
    - "gated group self attention for answer selection"
    - "interpretable question answering on knowledge bases and text"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "on the validity of self attention as explanation in transformer models"
    - "is attention interpretable"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "on variational bounds of mutual information"
    - "universal adversarial triggers for attacking and analyzing nlp"

Connected through hyperedge HE-2 (induced by source paper "i love your chain mail making knights smile in a fantasy game world open domain goal orientated dialogue agents"):
  with members:
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "hierarchical decision making by generating and following natural language instructions"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "learning to speak and act in a fantasy text adventure game"
    - "countering language drift via visual grounding"
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "target guided open domain conversation"
    - "textworld a learning environment for text based games"

Connected through hyperedge HE-3 (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"
    - "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"
    - "attentive pooling networks"

Connected through hyperedge HE-4 (induced by source paper "structural aware sentence similarity with recursive optimal transport"):
  with members:
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "context mover s distance barycenters optimal transport of contexts for building representations"
    - "word mover s embedding from word2vec to document embedding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "parameter free sentence embedding via orthogonal basis"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"

Connected through hyperedge HE-5 (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-6 (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"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "retrieve and refine improved sequence generation models for dialogue"

Connected through hyperedge HE-7 (induced by source paper "the dialogue dodecathlon open domain knowledge and image grounded conversational agents"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "lxmert learning cross modality encoder representations from transformers"
    - "the curious case of neural text degeneration"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "real or fake learning to discriminate machine from human generated text"

Connected through hyperedge HE-8 (induced by source paper "learning semantic sentence representations from visually grounded language without lexical knowledge"):
  with members:
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "using sparse semantic embeddings learned from multimodal text and image data to model human conceptual knowledge"
    - "predicting visual features from text for image and video caption retrieval"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "order embeddings and character level convolutions for multimodal alignment"
    - "learning visually grounded sentence representations"
    - "lstm a search space odyssey"
    - "supervised learning of universal sentence representations from natural language inference data"

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)