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: "neural programmer inducing latent programs with gradient descent"
  Abstract: Deep neural networks have achieved impressive supervised classification performance in many tasks including image recognition, speech recognition, and sequence to sequence learning. However, this success has not been translated to applications like question answering that may involve complex arithmetic and logic reasoning. A major limitation of these models is in their inability to learn even simple arithmetic and logic operations. For example, it has been shown that neural networks fail to learn to add two binary numbers reliably. In this work, we propose Neural Programmer, an end-to-end differentiable neural network augmented with a small set of basic arithmetic and logic operations. Neural Programmer can call these augmented operations over several steps, thereby inducing compositional programs that are more complex than the built-in operations. The model learns from a weak supervision signal which is the result of execution of the correct program, hence it does not require expensive annotation of the correct program itself. The decisions of what operations to call, and what data segments to apply to are inferred by Neural Programmer. Such decisions, during training, are done in a differentiable fashion so that the entire network can be trained jointly by gradient descent. We find that training the model is difficult, but it can be greatly improved by adding random noise to the gradient. On a fairly complex synthetic table-comprehension dataset, traditional recurrent networks and attentional models perform poorly while Neural Programmer typically obtains nearly perfect accuracy.

Connected through hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "towards modular and programmable architecture search"
    - "weight agnostic neural networks"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "web table extraction retrieval and augmentation a survey"):
  with members:
    - "auto completion for data cells in relational tables"
    - "recommending related tables"
    - "tablenet an approach for determining fine grained relations for wikipedia tables"
    - "table2vec neural word and entity embeddings for table population and retrieval"
    - "generating titles for web tables"
    - "ad hoc table retrieval using semantic similarity"
    - "on the fly table generation"
    - "entitables smart assistance for entity focused tables"

Connected through hyperedge HE-3 (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-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 "neural logic machines"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "deepproblog neural probabilistic logic programming"
    - "logical rule induction and theory learning using neural theorem proving"
    - "tunneling neural perception and logic reasoning through abductive learning"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "learning explanatory rules from noisy data"
    - "robustfill neural program learning under noisy i o"
    - "differentiable learning of logical rules for knowledge base reasoning"

Connected through hyperedge HE-6 (induced by source paper "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"):
  with members:
    - "from recognition to cognition visual commonsense reasoning"
    - "annotation artifacts in natural language inference data"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "the web as a knowledge base for answering complex questions"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-7 (induced by source paper "continual and multi task architecture search"):
  with members:
    - "the evolved transformer"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "efficient neural architecture search via parameter sharing"
    - "reinforced video captioning with entailment rewards"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-8 (induced by source paper "tabfact a large scale dataset for table based fact verification"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "a corpus for reasoning about natural language grounded in photographs"

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.LG(Machine Learning)