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: "analyzing assumptions in conversation disentanglement research through the lens of a new dataset and model"
  Abstract: Disentangling conversations mixed together in a single stream of messages is a difficult task with no large annotated datasets. We created a new dataset that is 25 times the size of any previous publicly available resource, has samples of conversation from 152 points in time across a decade, and is annotated with both threads and a within-thread reply-structure graph. We also developed a new neural network model, which extracts conversation threads substantially more accurately than prior work. Using our annotated data and our model we tested assumptions in prior work, revealing major issues in heuristically constructed resources, and identifying how small datasets have biased our understanding of multi-party multi-conversation chat.

Connected through hyperedge HE-1 (induced by source paper "sequential neural networks for noetic end to end response selection"):
  with members:
    - "knowledge incorporating esim models for response selection in retrieval based dialog systems"
    - "sequential attention based network for noetic end to end response selection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "enhancing sentence embedding with generalized pooling"
    - "neural natural language inference models enhanced with external knowledge"
    - "attention is all you need"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-2 (induced by source paper "knowledge incorporating esim models for response selection in retrieval based dialog systems"):
  with members:
    - "enhance word representation for out of vocabulary on ubuntu dialogue corpus"
    - "key value retrieval networks for task oriented dialogue"
    - "a retrieval based dialogue system utilizing utterance and context embeddings"
    - "a knowledge grounded neural conversation model"
    - "augmenting end to end dialog systems with commonsense knowledge"
    - "enhanced lstm for natural language inference"
    - "lstm based mixture of experts for knowledge aware dialogues"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"

Connected through hyperedge HE-3 (induced by source paper "sequential attention based network for noetic end to end response selection"):
  with members:
    - "modeling multi turn conversation with deep utterance aggregation"
    - "enhancing sentence embedding with generalized pooling"
    - "neural natural language inference models enhanced with external knowledge"
    - "attention is all you need"
    - "advances in pre training distributed word representations"
    - "a structured self attentive sentence embedding"
    - "recurrent neural network based sentence encoder with gated attention for natural language inference"
    - "enhanced lstm for natural language inference"

Connected through hyperedge HE-4 (induced by source paper "dialog system technology challenge 7"):
  with members:
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "audio visual scene aware dialog avsd challenge at dstc7"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end conversation modeling track in dstc6"
    - "attention strategies for multi source sequence to sequence learning"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "enhanced lstm for natural language inference"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "learning multi level information for dialogue response selection by highway recurrent transformer"):
  with members:
    - "modeling multi turn conversation with deep utterance aggregation"
    - "attention is all you need"
    - "enriching word vectors with subword information"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "improved deep learning baselines for ubuntu corpus dialogs"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "rap net recurrent attention pooling networks for dialogue response selection"):
  with members:
    - "multi cast attention networks for retrieval based question answering and response prediction"
    - "advances in pre training distributed word representations"
    - "attentive pooling networks"
    - "applying deep learning to answer selection a study and an open task"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "reasoning about entailment with neural attention"
    - "a deep architecture for semantic matching with multiple positional sentence representations"
    - "lstm based deep learning models for non factoid answer selection"

Connected through hyperedge HE-7 (induced by source paper "building sequential inference models for end to end response selection"):
  with members:
    - "deep contextualized word representations"
    - "enhance word representation for out of vocabulary on ubuntu dialogue corpus"
    - "bilateral multi perspective matching for natural language sentences"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "enhanced lstm for natural language inference"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "neural responding machine for short text conversation"

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)