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 to probabilistic programs for situated question answering"
  Abstract: Situated question answering is the problem of answering questions about an environment such as an image or diagram. This problem requires jointly interpreting a question and an environment using background knowledge to select the correct answer. We present Parsing to Probabilistic Programs (P3), a novel situated question answering model that can use background knowledge and global features of the question/environment interpretation while retaining efficient approximate inference. Our key insight is to treat semantic parses as probabilistic programs that execute nondeterministically and whose possible executions represent environmental uncertainty. We evaluate our approach on a new, publicly-released data set of 5000 science diagram questions, outperforming several competitive classical and neural baselines.

Connected through hyperedge HE-1 (induced by source paper "reasoning driven question answering for natural language understanding"):
  with members:
    - "on the capabilities and limitations of reasoning for natural language understanding"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "going on a vacation takes longer than going for a walk a study of temporal commonsense understanding"
    - "cogcomptime a tool for understanding time in natural language text"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "multi hop knowledge graph reasoning with reward shaping"

Connected through hyperedge HE-2 (induced by source paper "from f to a on the n y regents science exams an overview of the aristo project"):
  with members:
    - "mathqa towards interpretable math word problem solving with operation based formalisms"
    - "quartz an open domain dataset of qualitative relationship questions"
    - "repurposing entailment for multi hop question answering tasks"
    - "roberta a robustly optimized bert pretraining approach"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hypothesis only baselines in natural language inference"

Connected through hyperedge HE-3 (induced by source paper "question answering as global reasoning over semantic abstractions"):
  with members:
    - "question answering through transfer learning from large fine grained supervision data"
    - "adversarial examples for evaluating reading comprehension systems"
    - "answering complex questions using open information extraction"
    - "question answering via integer programming over semi structured knowledge"
    - "a decomposable attention model for natural language inference"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "neural semantic role labeling with dependency path embeddings"

Connected through hyperedge HE-4 (induced by source paper "structvae tree structured latent variable models for semi supervised semantic parsing"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "neural amr sequence to sequence models for parsing and generation"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "learning a neural semantic parser from user feedback"
    - "abstract syntax networks for code generation and semantic parsing"
    - "learning structured natural language representations for semantic parsing"

Connected through hyperedge HE-5 (induced by source paper "structured set matching networks for one shot part labeling"):
  with members:
    - "scnet learning semantic correspondence"
    - "sequence to sequence learning as beam search optimization"
    - "a diagram is worth a dozen images"
    - "globally normalized transition based neural networks"
    - "probabilistic neural programs"
    - "matching networks for one shot learning"
    - "contextual rnn gans for abstract reasoning diagram generation"
    - "computing the stereo matching cost with a convolutional neural network"

Connected through hyperedge HE-6 (induced by source paper "a syntactic neural model for general purpose code generation"):
  with members:
    - "language to logical form with neural attention"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "deepcoder learning to write programs"
    - "latent predictor networks for code generation"
    - "data recombination for neural semantic parsing"
    - "semantic parsing with semi supervised sequential autoencoders"
    - "swim synthesizing what i mean code search and idiomatic snippet synthesis"

Connected through hyperedge HE-7 (induced by source paper "neural architecture for question answering using a knowledge graph and web corpus"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "deep joint entity disambiguation with local neural attention"
    - "co pacrr a context aware neural ir model for ad hoc retrieval"
    - "word entity duet representations for document ranking"
    - "language to logical form with neural attention"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "on the sample complexity of end to end training vs semantic abstraction training"
    - "learning to compose neural networks for question answering"

Connected through hyperedge HE-8 (induced by source paper "dynamic graph generation network generating relational knowledge from diagrams"):
  with members:
    - "vip cnn visual phrase guided convolutional neural network"
    - "scene graph generation by iterative message passing"
    - "gated graph sequence neural networks"
    - "a diagram is worth a dozen images"
    - "the more you know using knowledge graphs for image classification"
    - "dynamic memory networks for visual and textual question answering"
    - "visual relationship detection with language priors"
    - "deep residual learning for image recognition"

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)