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: "augmenting neural response generation with context aware topical attention"
  Abstract: Sequence-to-Sequence (Seq2Seq) models have witnessed a notable success in generating natural conversational exchanges. Notwithstanding the syntactically well-formed responses generated by these neural network models, they are prone to be acontextual, short and generic. In this work, we introduce a Topical Hierarchical Recurrent Encoder Decoder (THRED), a novel, fully data-driven, multi-turn response generation system intended to produce contextual and topic-aware responses. Our model is built upon the basic Seq2Seq model by augmenting it with a hierarchical joint attention mechanism that incorporates topical concepts and previous interactions into the response generation. To train our model, we provide a clean and high-quality conversational dataset mined from Reddit comments. We evaluate THRED on two novel automated metrics, dubbed Semantic Similarity and Response Echo Index, as well as with human evaluation. Our experiments demonstrate that the proposed model is able to generate more diverse and contextually relevant responses compared to the strong baselines.

Connected through hyperedge HE-1 (induced by source paper "low resource knowledge grounded dialogue generation"):
  with members:
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "mass masked sequence to sequence pre training for language generation"
    - "disentangling factors of variation using few labels"
    - "learning to select knowledge for response generation in dialog systems"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents"):
  with members:
    - "evaluating coherence in dialogue systems using entailment"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "rankme reliable human ratings for natural language generation"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "a knowledge grounded neural conversation model"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "detecting asks in se attacks impact of linguistic and structural knowledge"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"
    - "investigating evaluation of open domain dialogue systems with human generated multiple references"
    - "allennlp a deep semantic natural language processing platform"
    - "neural approaches to conversational ai"

Connected through hyperedge HE-4 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "improving neural response diversity with frequency aware cross entropy loss"
    - "an adversarial approach to high quality sentiment controlled neural dialogue generation"
    - "topic guided variational autoencoders for text generation"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "knowledge aware conversation generation with reasoning on augmented graph"
    - "reinforcement learning based emotional editing constraint conversation generation"
    - "towards an engine for lifelong interactive knowledge learning in human machine conversations"

Connected through hyperedge HE-5 (induced by source paper "emotional neural language generation grounded in situational contexts"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "the curious case of neural text degeneration"
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "encoder agnostic adaptation for conditional language generation"
    - "towards best experiment design for evaluating dialogue system output"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-6 (induced by source paper "evaluating coherence in dialogue systems using entailment"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "dialogue natural language inference"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "towards best experiment design for evaluating dialogue system output"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "evaluating coherence in dialogue systems using entailment"
    - "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"
    - "rankme reliable human ratings for natural language generation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "hierarchical recurrent attention network for response generation"
    - "towards an automatic turing test learning to evaluate dialogue responses"

Connected through hyperedge HE-8 (induced by source paper "who did they respond to conversation structure modeling using masked hierarchical transformer"):
  with members:
    - "gsn a graph structured network for multi party dialogues"
    - "domain adaptive training bert for response selection"
    - "a large scale corpus for conversation disentanglement"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural natural language inference models enhanced with external knowledge"
    - "attention is all you need"
    - "a decomposable attention model 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)