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: "addressee and response selection in multi party conversations with speaker interaction rnns"
  Abstract: In this paper, we study the problem of addressee and response selection in multi-party conversations. Understanding multi-party conversations is challenging because of complex speaker interactions: multiple speakers exchange messages with each other, playing different roles (sender, addressee, observer), and these roles vary across turns. To tackle this challenge, we propose the Speaker Interaction Recurrent Neural Network (SI-RNN). Whereas the previous state-of-the-art system updated speaker embeddings only for the sender, SI-RNN uses a novel dialog encoder to update speaker embeddings in a role-sensitive way. Additionally, unlike the previous work that selected the addressee and response separately, SI-RNN selects them jointly by viewing the task as a sequence prediction problem. Experimental results show that SI-RNN significantly improves the accuracy of addressee and response selection, particularly in complex conversations with many speakers and responses to distant messages many turns in the past.

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 "personalized dialogue generation with diversified traits"):
  with members:
    - "training millions of personalized dialogue agents"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "assigning personality identity to a chatting machine for coherent conversation generation"
    - "low rank rnn adaptation for context aware language modeling"
    - "personalization in goal oriented dialog"

Connected through hyperedge HE-3 (induced by source paper "gsn a graph structured network for multi party dialogues"):
  with members:
    - "graph to sequence learning using gated graph neural networks"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "assigning personality identity to a chatting machine for coherent conversation generation"
    - "dynamic routing between capsules"
    - "towards neural speaker modeling in multi party conversation the task dataset and models"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "a persona based neural conversation model"
    - "lstm based conversation models"

Connected through hyperedge HE-4 (induced by source paper "dual supervised learning for natural language understanding and generation"):
  with members:
    - "dynamically context sensitive time decay attention for dialogue modeling"
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "integrating planning for task completion dialogue policy learning"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "investigating linguistic pattern ordering in hierarchical natural language generation"
    - "dual supervised learning"
    - "listening while speaking speech chain by deep learning"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"

Connected through hyperedge HE-5 (induced by source paper "incorporating interlocutor aware context into response generation on multi party chatbots"):
  with members:
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "hierarchical rnn with static sentence level attention for text based speaker change detection"
    - "a hybrid architecture for multi party conversational systems"
    - "a persona based neural conversation model"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "a diversity promoting objective function for neural conversation models"

Connected through hyperedge HE-6 (induced by source paper "decay function free time aware attention to context and speaker indicator for spoken language understanding"):
  with members:
    - "dynamically context sensitive time decay attention for dialogue modeling"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
    - "speaker sensitive dual memory networks for multi turn slot tagging"
    - "speaker role contextual modeling for language understanding and dialogue policy learning"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "dynamically context sensitive time decay attention for dialogue modeling"):
  with members:
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "end to end task completion neural dialogue systems"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
    - "speaker role contextual modeling for language understanding and dialogue policy learning"
    - "knowledge as a teacher knowledge guided structural attention networks"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "dynamic memory networks for visual and textual question answering"
    - "learning end to end goal oriented dialog"

Connected through hyperedge HE-8 (induced by source paper "analyzing assumptions in conversation disentanglement research through the lens of a new dataset and model"):
  with members:
    - "enhanced lstm for natural language inference"
    - "piecewise latent variables for neural variational text processing"
    - "discovering conversational dependencies between messages in dialogs"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "objective criteria for the evaluation of clustering methods"

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)