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: "from language to programs bridging reinforcement learning and maximum marginal likelihood"
  Abstract: Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself. Consequently, we must search the space of programs for those that output the correct result, while not being misled by spurious programs: incorrect programs that coincidentally output the correct result. We connect two common learning paradigms, reinforcement learning (RL) and maximum marginal likelihood (MML), and then present a new learning algorithm that combines the strengths of both. The new algorithm guards against spurious programs by combining the systematic search traditionally employed in MML with the randomized exploration of RL, and by updating parameters such that probability is spread more evenly across consistent programs. We apply our learning algorithm to a new neural semantic parser and show significant gains over existing state-of-the-art results on a recent context-dependent semantic parsing task.

Connected through hyperedge HE-1 (induced by source paper "why build an assistant in minecraft"):
  with members:
    - "craftassist a framework for dialogue enabled interactive agents"
    - "habitat a platform for embodied ai research"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "towards deep conversational recommendations"
    - "learning to ask questions in open domain conversational systems with typed decoders"

Connected through hyperedge HE-2 (induced by source paper "introduction to neural network based approaches for question answering over knowledge graphs"):
  with members:
    - "pretrained transformers for simple question answering over knowledge graphs"
    - "learning to generalize from sparse and underspecified rewards"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "practical semantic parsing for spoken language understanding"
    - "question generation from sql queries improves neural semantic parsing"
    - "towards a question answering system over the semantic web"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"

Connected through hyperedge HE-3 (induced by source paper "learning to generalize from sparse and underspecified rewards"):
  with members:
    - "self supervised generalisation with meta auxiliary learning"
    - "reward shaping via meta learning"
    - "on first order meta learning algorithms"
    - "multi task maximum entropy inverse reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "discourse aware neural rewards for coherent text generation"
    - "babyai first steps towards grounded language learning with a human in the loop"

Connected through hyperedge HE-4 (induced by source paper "probabilistic neural symbolic models for interpretable visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "evalai towards better evaluation systems for ai agents"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "houdini lifelong learning as program synthesis"
    - "neural guided deductive search for real time program synthesis from examples"

Connected through hyperedge HE-5 (induced by source paper "learning semantic parsers from denotations with latent structured alignments and abstract programs"):
  with members:
    - "learning to infer program sketches"
    - "learning to generalize from sparse and underspecified rewards"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "semantic parsing with syntax and table aware sql generation"
    - "allennlp a deep semantic natural language processing platform"
    - "decoupling structure and lexicon for zero shot semantic parsing"

Connected through hyperedge HE-6 (induced by source paper "editing based sql query generation for cross domain context dependent questions"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "recursive and clause wise decoding for complex and cross domain text to sql generation"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "sparc cross domain semantic parsing in context"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "question generation from sql queries improves neural semantic parsing"
    - "learning to map context dependent sentences to executable formal queries"
    - "seq2sql generating structured queries from natural language using reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "a view on deep reinforcement learning in system optimization"):
  with members:
    - "neural packet classification"
    - "neo a learned query optimizer"
    - "reinforced genetic algorithm learning for optimizing computation graphs"
    - "a survey on compiler autotuning using machine learning"
    - "placeto learning generalizable device placement algorithms for distributed machine learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "iroko a framework to prototype reinforcement learning for data center traffic control"
    - "seq2sql generating structured queries from natural language using reinforcement learning"

Connected through hyperedge HE-8 (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"
    - "dual supervised learning"

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.AI(Artificial Intelligence)