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: "learning cross lingual distributed logical representations for semantic parsing"
  Abstract: With the development of several multilingual datasets used for semantic parsing, recent research efforts have looked into the problem of learning semantic parsers in a multilingual setup. However, how to improve the performance of a monolingual semantic parser for a specific language by leveraging data annotated in different languages remains a research question that is under-explored. In this work, we present a study to show how learning distributed representations of the logical forms from data annotated in different languages can be used for improving the performance of a monolingual semantic parser. We extend two existing monolingual semantic parsers to incorporate such cross-lingual distributed logical representations as features. Experiments show that our proposed approach is able to yield improved semantic parsing results on the standard multilingual GeoQuery dataset.

Connected through hyperedge HE-1 (induced by source paper "mining commonsense facts from the physical world"):
  with members:
    - "joint detection and location of english puns"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "aligning cross lingual entities with multi aspect information"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "n ary relation extraction using graph state lstm"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "automatic extraction of commonsense locatednear knowledge"
    - "fast and accurate entity recognition with iterated dilated convolutions"

Connected through hyperedge HE-2 (induced by source paper "text2math end to end parsing text into math expressions"):
  with members:
    - "joint detection and location of english puns"
    - "translating a math word problem to an expression tree"
    - "semantic parsing with syntax and table aware sql generation"
    - "coarse to fine decoding for neural semantic parsing"
    - "a meaning based statistical english math word problem solver"
    - "mapping to declarative knowledge for word problem solving"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "language to logical form with neural attention"

Connected through hyperedge HE-3 (induced by source paper "quantity tagger a latent variable sequence labeling approach to solving addition subtraction word problems"):
  with members:
    - "joint detection and location of english puns"
    - "translating a math word problem to an expression tree"
    - "unit dependency graph and its application to arithmetic word problem solving"
    - "neural architectures for named entity recognition"
    - "solving general arithmetic word problems"

Connected through hyperedge HE-4 (induced by source paper "dependency based hybrid trees for semantic parsing"):
  with members:
    - "universal semantic parsing"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "learning structured natural language representations for semantic parsing"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "language to logical form with neural attention"
    - "data recombination for neural semantic parsing"
    - "neural architectures for named entity recognition"
    - "getting more out of syntax with props"

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)