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: "semantic parsing with semi supervised sequential autoencoders"
  Abstract: We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing. The unsupervised component is based on a generative model in which latent sentences generate the unpaired logical forms. We apply this method to a number of semantic parsing tasks focusing on domains with limited access to labelled training data and extend those datasets with synthetically generated logical forms.

Connected through hyperedge HE-1 (induced by source paper "exploring neural models for parsing natural language into first order logic"):
  with members:
    - "probing neural network comprehension of natural language arguments"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "semantic parsing with syntax and table aware sql generation"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"
    - "attention is all you need"
    - "found in translation reconstructing phylogenetic language trees from translations"
    - "learning a neural semantic parser from user feedback"
    - "robust incremental neural semantic graph parsing"

Connected through hyperedge HE-2 (induced by source paper "don t paraphrase detect rapid and effective data collection for semantic parsing"):
  with members:
    - "learning to learn semantic parsers from natural language supervision"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "end to end open domain question answering with"
    - "practical semantic parsing for spoken language understanding"
    - "building a neural semantic parser from a domain ontology"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "building a conversational agent overnight with dialogue self play"

Connected through hyperedge HE-3 (induced by source paper "jointly learning semantic parser and natural language generator via dual information maximization"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "coarse to fine decoding for neural semantic parsing"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "code2seq generating sequences from structured representations of code"
    - "triangle generative adversarial networks"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"

Connected through hyperedge HE-4 (induced by source paper "learning neural sequence to sequence models from weak feedback with bipolar ramp loss"):
  with members:
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "improving a neural semantic parser by counterfactual learning from human bandit feedback"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "semi supervised qa with generative domain adaptive nets"
    - "coupling distributed and symbolic execution for natural language queries"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"

Connected through hyperedge HE-5 (induced by source paper "pre learning environment representations for data efficient neural instruction following"):
  with members:
    - "model based reinforcement learning for atari"
    - "the fast and the flexible training neural networks to learn to follow instructions from small data"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "analogs of linguistic structure in deep representations"
    - "natural language does not emerge naturally in multi agent dialog"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-6 (induced by source paper "neural semantic parsing with anonymization for command understanding in general purpose service robots"):
  with members:
    - "improving semantic parsing for task oriented dialog"
    - "from commands to goal based dialogs a roadmap to achieve natural language interaction in robocup home"
    - "improving grounded natural language understanding through human robot dialog"
    - "practical semantic parsing for spoken language understanding"
    - "improving text to sql evaluation methodology"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "improving grounded natural language understanding through human robot dialog"):
  with members:
    - "teaching robots to imitate a human with no on teacher sensors what are the key challenges"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "neural amr sequence to sequence models for parsing and generation"
    - "learning interpretable spatial operations in a rich 3d blocks world"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "language to logical form with neural attention"

Connected through hyperedge HE-8 (induced by source paper "semantic parsing with syntax and table aware sql generation"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "inferring and executing programs for visual reasoning"
    - "bidirectional attention for sql generation"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "learning to reason end to end module networks for visual question answering"
    - "weakly supervised semantic parsing with abstract examples"
    - "type and content driven synthesis of sql queries from natural language"

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)