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: "neural compositional denotational semantics for question answering"
  Abstract: Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph (KG), which is inspired by formal approaches to semantics. Each span of text is represented by a denotation in a KG and a vector that captures ungrounded aspects of meaning. Learned composition modules recursively combine constituent spans, culminating in a grounding for the complete sentence which answers the question. For example, to interpret "not green", the model represents "green" as a set of KG entities and "not" as a trainable ungrounded vector---and then uses this vector to parameterize a composition function that performs a complement operation. For each sentence, we build a parse chart subsuming all possible parses, allowing the model to jointly learn both the composition operators and output structure by gradient descent from end-task supervision. The model learns a variety of challenging semantic operators, such as quantifiers, disjunctions and composed relations, and infers latent syntactic structure. It also generalizes well to longer questions than seen in its training data, in contrast to RNN, its tree-based variants, and semantic parsing baselines.

Connected through hyperedge HE-1 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base"):
  with members:
    - "generating logical forms from graph representations of text and entities"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "neural query language a knowledge base query language for tensorflow"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "improving text to sql evaluation methodology"
    - "embedding logical queries on knowledge graphs"
    - "open domain question answering using early fusion of knowledge bases and text"

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 "differentiable representations for multihop inference rules"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "deep sets"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "variational reasoning for question answering with knowledge graph"
    - "tensorlog deep learning meets probabilistic dbs"

Connected through hyperedge HE-4 (induced by source paper "neural module networks for reasoning over text"):
  with members:
    - "giving bert a calculator finding operations and arguments with reading comprehension"
    - "sg net syntax guided machine reading comprehension"
    - "self assembling modular networks for interpretable multi hop reasoning"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "a multi type multi span network for reading comprehension that requires discrete reasoning"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-5 (induced by source paper "systematic generalization what is required and can it be learned"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "jump to better conclusions scan both left and right"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"

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)