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: "sling a framework for frame semantic parsing"
  Abstract: We describe SLING, a framework for parsing natural language into semantic frames. SLING supports general transition-based, neural-network parsing with bidirectional LSTM input encoding and a Transition Based Recurrent Unit (TBRU) for output decoding. The parsing model is trained end-to-end using only the text tokens as input. The transition system has been designed to output frame graphs directly without any intervening symbolic representation. The SLING framework includes an efficient and scalable frame store implementation as well as a neural network JIT compiler for fast inference during parsing. SLING is implemented in C++ and it is available for download on GitHub.

Connected through hyperedge HE-1 (induced by source paper "differentiable reasoning over a virtual knowledge base"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "bag bi directional attention entity graph convolutional network for multi hop reasoning question answering"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "neural query language a knowledge base query language for tensorflow"
    - "multi hop paragraph retrieval for open domain question answering"
    - "leveraging knowledge bases in lstms for improving machine reading"

Connected through hyperedge HE-2 (induced by source paper "improving semantic parsing for task oriented dialog"):
  with members:
    - "deep contextualized word representations"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "semantic parsing for task oriented dialog using hierarchical representations"
    - "learning an executable neural semantic parser"
    - "learning a neural semantic parser from user feedback"
    - "abstract syntax networks for code generation and semantic parsing"
    - "learning structured natural language representations for semantic parsing"
    - "language to logical form with neural attention"

Connected through hyperedge HE-3 (induced by source paper "querying knowledge via multi hop english questions"):
  with members:
    - "variational reasoning for question answering with knowledge graph"
    - "key value memory networks for directly reading documents"
    - "a survey and classification of controlled natural languages"
    - "question answering with subgraph embeddings"

Connected through hyperedge HE-4 (induced by source paper "knowledge authoring and question answering with kalm"):
  with members:
    - "variational reasoning for question answering with knowledge graph"
    - "key value memory networks for directly reading documents"
    - "a survey and classification of controlled natural languages"

Connected through hyperedge HE-5 (induced by source paper "a task in a suit and a tie paraphrase generation with semantic augmentation"):
  with members:
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "building a conversational agent overnight with dialogue self play"
    - "linguistically informed self attention for semantic role labeling"
    - "a continuously growing dataset of sentential paraphrases"
    - "attention is all you need"
    - "multi channel encoder for neural machine translation"
    - "a deep generative framework for paraphrase generation"
    - "neural paraphrase generation with stacked residual lstm networks"

Connected through hyperedge HE-6 (induced by source paper "scalable semantic querying of text"):
  with members:
    - "happydb a corpus of 100 000 crowdsourced happy moments"
    - "efficient indexing and querying over syntactically annotated trees"
    - "natural language processing almost from scratch"
    - "a universal part of speech tagset"

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)