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: "compositional semantic parsing on semi structured tables"
  Abstract: Two important aspects of semantic parsing for question answering are the breadth of the knowledge source and the depth of logical compositionality. While existing work trades off one aspect for another, this paper simultaneously makes progress on both fronts through a new task: answering complex questions on semi-structured tables using question-answer pairs as supervision. The central challenge arises from two compounding factors: the broader domain results in an open-ended set of relations, and the deeper compositionality results in a combinatorial explosion in the space of logical forms. We propose a logical-form driven parsing algorithm guided by strong typing constraints and show that it obtains significant improvements over natural baselines. For evaluation, we created a new dataset of 22,033 complex questions on Wikipedia tables, which is made publicly available.

Connected through hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"

Connected through hyperedge HE-2 (induced by source paper "break it down a question understanding benchmark"):
  with members:
    - "bridging the semantic gap with sql query logs in natural language interfaces to databases"
    - "compositional questions do not necessitate multi hop reasoning"
    - "understanding dataset design choices for multi hop reasoning"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "answering complex open domain questions through iterative query generation"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"

Connected through hyperedge HE-3 (induced by source paper "manymodalqa modality disambiguation and qa over diverse inputs"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "universal sentence encoder"
    - "adversarial tableqa attention supervision for question answering on tables"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "exploring neural models for parsing natural language into first order logic"):
  with members:
    - "probing neural network comprehension of natural language arguments"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "semantic parsing with syntax and table aware sql generation"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"
    - "attention is all you need"
    - "found in translation reconstructing phylogenetic language trees from translations"
    - "learning a neural semantic parser from user feedback"
    - "robust incremental neural semantic graph parsing"

Connected through hyperedge HE-5 (induced by source paper "plotqa reasoning over scientific plots"):
  with members:
    - "towards vqa models that can read"
    - "bilinear attention networks"
    - "dvqa understanding data visualizations via question answering"
    - "scatteract automated extraction of data from scatter plots"
    - "neural multi step reasoning for question answering on semi structured tables"
    - "figureqa an annotated figure dataset for visual reasoning"
    - "mask r cnn"
    - "hadamard product for low rank bilinear pooling"

Connected through hyperedge HE-6 (induced by source paper "schema2qa answering complex queries on the structured web with a neural model"):
  with members:
    - "genie a generator of natural language semantic parsers for virtual assistant commands"
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "the natural language decathlon multitask learning as question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"

Connected through hyperedge HE-7 (induced by source paper "web table extraction retrieval and augmentation a survey"):
  with members:
    - "auto completion for data cells in relational tables"
    - "recommending related tables"
    - "tablenet an approach for determining fine grained relations for wikipedia tables"
    - "table2vec neural word and entity embeddings for table population and retrieval"
    - "generating titles for web tables"
    - "ad hoc table retrieval using semantic similarity"
    - "on the fly table generation"
    - "entitables smart assistance for entity focused tables"

Connected through hyperedge HE-8 (induced by source paper "flowsense a natural language interface for visual data exploration within a dataflow system"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "recent trends in deep learning based natural language processing"
    - "iris a conversational agent for complex tasks"
    - "a brief survey of text mining classification clustering and extraction techniques"

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)