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: "not all dialogues are created equal instance weighting for neural conversational models"
  Abstract: Neural conversational models require substantial amounts of dialogue data for their parameter estimation and are therefore usually learned on large corpora such as chat forums or movie subtitles. These corpora are, however, often challenging to work with, notably due to their frequent lack of turn segmentation and the presence of multiple references external to the dialogue itself. This paper shows that these challenges can be mitigated by adding a weighting model into the architecture. The weighting model, which is itself estimated from dialogue data, associates each training example to a numerical weight that reflects its intrinsic quality for dialogue modelling. At training time, these sample weights are included into the empirical loss to be minimised. Evaluation results on retrieval-based models trained on movie and TV subtitles demonstrate that the inclusion of such a weighting model improves the model performance on unsupervised metrics.

Connected through hyperedge HE-1 (induced by source paper "learning from easy to complex adaptive multi curricula learning for neural dialogue generation"):
  with members:
    - "improving neural conversational models with entropy based data filtering"
    - "recosa detecting the relevant contexts with self attention for multi turn dialogue generation"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "competence based curriculum learning for neural machine translation"
    - "simple and effective curriculum pointer generator networks for reading comprehension over long narratives"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "curriculum learning by transfer learning theory and experiments with deep networks"

Connected through hyperedge HE-2 (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"
    - "learning matching models with weak supervision for response selection in retrieval based chatbots"
    - "iterative learning with open set noisy labels"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "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-3 (induced by source paper "the rapidly changing landscape of conversational agents"):
  with members:
    - "quantized dialog language model for goal oriented conversational systems"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "toward continual learning for conversational agents"
    - "a knowledge grounded neural conversation model"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "adversarial learning for neural dialogue generation"
    - "augmenting end to end dialog systems with commonsense knowledge"

Connected through hyperedge HE-4 (induced by source paper "strategy of the negative sampling for training retrieval based dialogue systems"):
  with members:
    - "improved deep learning baselines for ubuntu corpus dialogs"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"

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)