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: "analogical reasoning on chinese morphological and semantic relations"
  Abstract: Analogical reasoning is effective in capturing linguistic regularities. This paper proposes an analogical reasoning task on Chinese. After delving into Chinese lexical knowledge, we sketch 68 implicit morphological relations and 28 explicit semantic relations. A big and balanced dataset CA8 is then built for this task, including 17813 questions. Furthermore, we systematically explore the influences of vector representations, context features, and corpora on analogical reasoning. With the experiments, CA8 is proved to be a reliable benchmark for evaluating Chinese word embeddings.

Connected through hyperedge HE-1 (induced by source paper "dynamic knowledge routing network for target guided open domain conversation"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "end to end knowledge routed relational dialogue system for automatic diagnosis"
    - "target guided open domain conversation"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "towards deep conversational recommendations"
    - "emergent communication through negotiation"
    - "conversational ai the science behind the alexa prize"
    - "dailydialog a manually labelled multi turn dialogue dataset"

Connected through hyperedge HE-2 (induced by source paper "on the general value of evidence and bilingual scene text visual question answering"):
  with members:
    - "towards vqa models that can read"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "chinese street view text large scale chinese text reading with partially supervised learning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "visual question answering datasets algorithms and future challenges"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"

Connected through hyperedge HE-3 (induced by source paper "can ner convolutional attention network for chinese named entity recognition"):
  with members:
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "chinese ner using lattice lstm"
    - "design challenges and misconceptions in neural sequence labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial learning for chinese ner from crowd annotations"
    - "combining discrete and neural features for sequence labeling"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "investigating prior knowledge for challenging chinese machine reading comprehension"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "ernie enhanced representation through knowledge integration"
    - "geosqa a benchmark for scenario based question answering in the geography domain at high school level"
    - "pre training with whole word masking for chinese bert"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "chid a large scale chinese idiom dataset for cloze test"
    - "exploiting sentence embedding for medical question answering"
    - "coqa a conversational question answering challenge"

Connected through hyperedge HE-5 (induced by source paper "bipar a bilingual parallel dataset for multilingual and cross lingual reading comprehension on novels"):
  with members:
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "coqa a conversational question answering challenge"
    - "multilingual extractive reading comprehension by runtime machine translation"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a span extraction dataset for chinese machine reading comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-6 (induced by source paper "induction networks for few shot text classification"):
  with members:
    - "lifelong domain word embedding via meta learning"
    - "investigating capsule networks with dynamic routing for text classification"
    - "zero shot user intent detection via capsule neural networks"
    - "diverse few shot text classification with multiple metrics"
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"
    - "dynamic routing between capsules"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-7 (induced by source paper "don t just scratch the surface enhancing word representations for korean with hanja"):
  with members:
    - "cross lingual language model pretraining"
    - "xnli evaluating cross lingual sentence representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a sub character architecture for korean language processing"
    - "opennmt open source toolkit for neural machine translation"
    - "a syllable based technique for word embeddings of korean words"
    - "word translation without parallel data"

Connected through hyperedge HE-8 (induced by source paper "generating multiple diverse responses with multi mapping and posterior mapping selection"):
  with members:
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "generating multiple diverse responses for short text conversation"
    - "towards explainable and controllable open domain dialogue generation with dialogue acts"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a simple fast diverse decoding algorithm for neural generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "generating sentences from a continuous space"

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)