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: "latent attention for if then program synthesis"
  Abstract: Automatic translation from natural language descriptions into programs is a longstanding challenging problem. In this work, we consider a simple yet important sub-problem: translation from textual descriptions to If-Then programs. We devise a novel neural network architecture for this task which we train end-to-end. Specifically, we introduce Latent Attention, which computes multiplicative weights for the words in the description in a two-stage process with the goal of better leveraging the natural language structures that indicate the relevant parts for predicting program elements. Our architecture reduces the error rate by 28.57% compared to prior art. We also propose a one-shot learning scenario of If-Then program synthesis and simulate it with our existing dataset. We demonstrate a variation on the training procedure for this scenario that outperforms the original procedure, significantly closing the gap to the model trained with all data.

Connected through hyperedge HE-1 (induced by source paper "evaluating sequence to sequence learning models for if then program synthesis"):
  with members:
    - "joey nmt a minimalist nmt toolkit for novices"
    - "opennmt neural machine translation toolkit"
    - "interactive semantic parsing for if then recipes via hierarchical reinforcement learning"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "attention is all you need"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (induced by source paper "from natural language instructions to complex processes issues in chaining trigger action rules"):
  with members:
    - "analyzing sentence fusion in abstractive summarization"
    - "interactive semantic parsing for if then recipes via hierarchical reinforcement learning"
    - "a syntactic neural model for general purpose code generation"
    - "language to logical form with neural attention"

Connected through hyperedge HE-3 (induced by source paper "genie a generator of natural language semantic parsers for virtual assistant commands"):
  with members:
    - "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"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "interactive semantic parsing for if then recipes via hierarchical reinforcement learning"
    - "deep contextualized word representations"
    - "fast and scalable expansion of natural language understanding functionality for intelligent agents"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-4 (induced by source paper "instructablecrowd creating if then rules for smartphones via conversations with the crowd"):
  with members:
    - "evorus a crowd powered conversational assistant built to automate itself over time"
    - "is there anything else i can help you with challenges in deploying an on demand crowd powered conversational agent"
    - "language to logical form with neural attention"

Connected through hyperedge HE-5 (induced by source paper "tree to tree neural networks for program translation"):
  with members:
    - "robustfill neural program learning under noisy i o"
    - "attention is all you need"
    - "towards neural machine translation with latent tree attention"
    - "towards string to tree neural machine translation"
    - "a survey of machine learning for big code and naturalness"
    - "towards synthesizing complex programs from input output examples"
    - "abstract syntax networks for code generation and semantic parsing"
    - "a syntactic neural model for general purpose code generation"

Connected through hyperedge HE-6 (induced by source paper "interactive semantic parsing for if then recipes via hierarchical reinforcement learning"):
  with members:
    - "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "deep dyna q integrating planning for task completion dialogue policy learning"
    - "neural approaches to conversational ai"
    - "video captioning via hierarchical reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "learning a neural semantic parser from user feedback"

Connected through hyperedge HE-7 (induced by source paper "executable trigger action comments"):
  with members:
    - "a neural architecture for generating natural language descriptions from source code changes"

Connected through hyperedge HE-8 (induced by source paper "bidirectional attention for sql generation"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "an encoder decoder framework translating natural language to database queries"
    - "language to logical form with neural attention"
    - "topic augmented neural network for short text conversation"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "adam a method for stochastic optimization"

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)