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 matching models with weak supervision for response selection in retrieval based chatbots"
  Abstract: We propose a method that can leverage unlabeled data to learn a matching model for response selection in retrieval-based chatbots. The method employs a sequence-to-sequence architecture (Seq2Seq) model as a weak annotator to judge the matching degree of unlabeled pairs, and then performs learning with both the weak signals and the unlabeled data. Experimental results on two public data sets indicate that matching models get significant improvements when they are learned with the proposed method.

Connected through hyperedge HE-1 (induced by source paper "improving multi turn response selection models with complementary last utterance selection by instance weighting"):
  with members:
    - "unsupervised context rewriting for open domain conversation"
    - "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems"
    - "iterative learning with open set noisy labels"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "not all dialogues are created equal instance weighting for neural conversational models"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "match srnn modeling the recursive matching structure with spatial rnn"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-2 (induced by source paper "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems"):
  with members:
    - "co sampling training robust networks for extremely noisy supervision"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "a sequential matching framework for multi turn response selection in retrieval based chatbots"
    - "fidelity weighted learning"
    - "a persona based neural conversation model"

Connected through hyperedge HE-3 (induced by source paper "learning fast matching models from weak annotations"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "neural models for information retrieval"
    - "a deep relevance matching model for ad hoc retrieval"
    - "a decomposable attention model for natural language inference"
    - "learning to match using local and distributed representations of text for web search"
    - "deep model compression distilling knowledge from noisy teachers"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "faq retrieval using query question similarity and bert based query answer relevance"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "squad 100 000 questions for machine comprehension of text"
    - "neural machine translation of rare words with subword units"
    - "lstm based deep learning models for non factoid answer selection"

Connected through hyperedge HE-5 (induced by source paper "modeling psychotherapy dialogues with kernelized hashcode representations a nonparametric information theoretic approach"):
  with members:
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "decoupling strategy and generation in negotiation dialogues"
    - "a hierarchical latent structure for variational conversation modeling"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "kernelized hashcode representations for relation extraction"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-6 (induced by source paper "a sequential matching framework for multi turn response selection in retrieval based chatbots"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "adversarial learning for neural dialogue generation"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "a decomposable attention model for natural language inference"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "enhanced lstm for natural language inference"

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)