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: "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
  Abstract: While conversing with chatbots, humans typically tend to ask many questions, a significant portion of which can be answered by referring to large-scale knowledge graphs (KG). While Question Answering (QA) and dialog systems have been studied independently, there is a need to study them closely to evaluate such real-world scenarios faced by bots involving both these tasks. Towards this end, we introduce the task of Complex Sequential QA which combines the two tasks of (i) answering factual questions through complex inferencing over a realistic-sized KG of millions of entities, and (ii) learning to converse through a series of coherently linked QA pairs. Through a labor intensive semi-automatic process, involving in-house and crowdsourced workers, we created a dataset containing around 200K dialogs with a total of 1.6M turns. Further, unlike existing large scale QA datasets which contain simple questions that can be answered from a single tuple, the questions in our dialogs require a larger subgraph of the KG. Specifically, our dataset has questions which require logical, quantitative, and comparative reasoning as well as their combinations. This calls for models which can: (i) parse complex natural language questions, (ii) use conversation context to resolve coreferences and ellipsis in utterances, (iii) ask for clarifications for ambiguous queries, and finally (iv) retrieve relevant subgraphs of the KG to answer such questions. However, our experiments with a combination of state of the art dialog and QA models show that they clearly do not achieve the above objectives and are inadequate for dealing with such complex real world settings. We believe that this new dataset coupled with the limitations of existing models as reported in this paper should encourage further research in Complex Sequential QA.

Connected through hyperedge HE-1 (induced by source paper "the keen universe an ecosystem for knowledge graph embeddings with a focus on reproducibility and transferability"):
  with members:
    - "el embeddings geometric construction of models for the description logic el"
    - "leveraging knowledge graph embedding techniques for industry 4 0 use cases"
    - "convolutional 2d knowledge graph embeddings"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"

Connected through hyperedge HE-2 (induced by source paper "amused a multi stream vector representation method for use in natural dialogue"):
  with members:
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a knowledge grounded neural conversation model"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "reinforced dynamic reasoning for conversational question generation"):
  with members:
    - "question generation from sql queries improves neural semantic parsing"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "coqa a conversational question answering challenge"
    - "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-4 (induced by source paper "multi task learning for conversational question answering over a large scale knowledge base"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "coarse to fine decoding for neural semantic parsing"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"
    - "abstract syntax networks for code generation and semantic parsing"
    - "learning structured natural language representations for semantic parsing"
    - "a syntactic neural model for general purpose code generation"
    - "language to logical form with neural attention"

Connected through hyperedge HE-5 (induced by source paper "coupling retrieval and meta learning for context dependent semantic parsing"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "dictionary guided editing networks for paraphrase generation"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "response generation by context aware prototype editing"
    - "mapping language to code in programmatic context"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-6 (induced by source paper "does gender matter towards fairness in dialogue systems"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "a survey on bias and fairness in machine learning"
    - "compositional fairness constraints for graph embeddings"
    - "scalable fair clustering"
    - "identifying and reducing gender bias in word level language models"
    - "build it break it fix it for dialogue safety robustness from adversarial human attack"
    - "say what i want towards the dark side of neural dialogue models"
    - "assessing gender bias in machine translation a case study with google translate"

Connected through hyperedge HE-7 (induced by source paper "using a kg copy network for non goal oriented dialogues"):
  with members:
    - "incorporating joint embeddings into goal oriented dialogues with multi task learning"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "improving retrieval modeling using cross convolution networks and multi frequency word embedding"
    - "a trustworthy responsible and interpretable system to handle chit chat in conversational bots"
    - "a knowledge grounded neural conversation model"
    - "chat detection in an intelligent assistant combining task oriented and non task oriented spoken dialogue systems"
    - "flexible end to end dialogue system for knowledge grounded conversation"

Connected through hyperedge HE-8 (induced by source paper "effective search of logical forms for weakly supervised knowledge based question answering"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "coarse to fine decoding for neural semantic parsing"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "attention is all you need"
    - "macro grammars and holistic triggering for efficient semantic parsing"
    - "inferring logical forms from denotations"
    - "key value memory networks for directly reading documents"

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)