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: "sdnet contextualized attention based deep network for conversational question answering"
  Abstract: Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context. Different from traditional single-turn machine reading comprehension (MRC) tasks, CQA includes passage comprehension, coreference resolution, and contextual understanding. In this paper, we propose an innovated contextualized attention-based deep neural network, SDNet, to fuse context into traditional MRC models. Our model leverages both inter-attention and self-attention to comprehend conversation context and extract relevant information from passage. Furthermore, we demonstrated a novel method to integrate the latest BERT contextual model. Empirical results show the effectiveness of our model, which sets the new state of the art result in CoQA leaderboard, outperforming the previous best model by 1.6% F1. Our ensemble model further improves the result by 2.7% F1.

Connected through hyperedge HE-1 (induced by source paper "ma dst multi attention based scalable dialog state tracking"):
  with members:
    - "hyst a hybrid approach for flexible and accurate dialogue state tracking"
    - "dialog state tracking a neural reading comprehension approach"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"

Connected through hyperedge HE-2 (induced by source paper "multimodal transformer networks for end to end video grounded dialogue systems"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "training millions of personalized dialogue agents"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "jointly localizing and describing events for dense video captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "patient knowledge distillation for bert model compression"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "model compression via distillation and quantization"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "reinforced dynamic reasoning for conversational question generation"):
  with members:
    - "question generation from sql queries improves neural semantic parsing"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "coqa a conversational question answering challenge"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "quac question answering in context"
    - "flowqa grasping flow in history for conversational machine comprehension"

Connected through hyperedge HE-5 (induced by source paper "review conversational reading comprehension"):
  with members:
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quac question answering in context"
    - "flowqa grasping flow in history for conversational machine comprehension"

Connected through hyperedge HE-6 (induced by source paper "attentive history selection for conversational question answering"):
  with members:
    - "bert with history answer embedding for conversational question answering"
    - "answer interaction in non factoid question answering systems"
    - "multi task deep neural networks for natural language understanding"
    - "user intent prediction in information seeking conversations"
    - "a deep look into neural ranking models for information retrieval"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-7 (induced by source paper "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"):
  with members:
    - "sql to text generation with graph to sequence model"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "contextualized non local neural networks for sequence learning"
    - "coqa a conversational question answering challenge"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "open domain question answering using early fusion of knowledge bases and text"

Connected through hyperedge HE-8 (induced by source paper "an empirical study of content understanding in conversational question answering"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "analyzing compositionality sensitivity of nli models"
    - "coqa a conversational question answering challenge"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"

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)