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: "evaluating induced ccg parsers on grounded semantic parsing"
  Abstract: We compare the effectiveness of four different syntactic CCG parsers for a semantic slot-filling task to explore how much syntactic supervision is required for downstream semantic analysis. This extrinsic, task-based evaluation provides a unique window to explore the strengths and weaknesses of semantics captured by unsupervised grammar induction systems. We release a new Freebase semantic parsing dataset called SPADES (Semantic PArsing of DEclarative Sentences) containing 93K cloze-style questions paired with answers. We evaluate all our models on this dataset. Our code and data are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "learning cross context entity representations from text"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "end to end neural entity linking"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "additive margin softmax for face verification"
    - "deeptype multilingual entity linking by neural type system evolution"

Connected through hyperedge HE-2 (induced by source paper "interpretable question answering on knowledge bases and text"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention is not explanation"
    - "compositional attention networks for interpretability in natural language question answering"
    - "are you tough enough framework for robustness validation of machine comprehension systems"
    - "manipulating and measuring model interpretability"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "key value memory networks for directly reading documents"
    - "the mythos of model interpretability"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised neural semantic parsing with a generative ranker"):
  with members:
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "universal semantic parsing"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "weakly supervised semantic parsing with abstract examples"
    - "learning a neural semantic parser from user feedback"
    - "a generative parser with a discriminative recognition algorithm"
    - "language to logical form with neural attention"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"

Connected through hyperedge HE-4 (induced by source paper "learning an executable neural semantic parser"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "universal semantic parsing"
    - "structured attention networks"
    - "learning structured text representations"
    - "learning to paraphrase for question answering"
    - "attention is all you need"
    - "making neural programming architectures generalize via recursion"

Connected through hyperedge HE-5 (induced by source paper "learning structured natural language representations for semantic parsing"):
  with members:
    - "recurrent neural network grammars"
    - "learning a natural language interface with neural programmer"
    - "rationalizing neural predictions"
    - "data recombination for neural semantic parsing"
    - "no need to pay attention simple recurrent neural networks work for answering simple questions"
    - "semantic parsing with semi supervised sequential autoencoders"
    - "question answering on freebase via relation extraction and textual evidence"
    - "show attend and tell neural image caption generation with visual attention"

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)