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: "recursive neural networks can learn logical semantics"
  Abstract: Tree-structured recursive neural networks (TreeRNNs) for sentence meaning have been successful for many applications, but it remains an open question whether the fixed-length representations that they learn can support tasks as demanding as logical deduction. We pursue this question by evaluating whether two such models---plain TreeRNNs and tree-structured neural tensor networks (TreeRNTNs)---can correctly learn to identify logical relationships such as entailment and contradiction using these representations. In our first set of experiments, we generate artificial data from a logical grammar and use it to evaluate the models' ability to learn to handle basic relational reasoning, recursive structures, and quantification. We then evaluate the models on the more natural SICK challenge data. Both models perform competitively on the SICK data and generalize well in all three experiments on simulated data, suggesting that they can learn suitable representations for logical inference in natural language.

Connected through hyperedge HE-1 (induced by source paper "vqa lol visual question answering under the lens of logic"):
  with members:
    - "towards vqa models that can read"
    - "lxmert learning cross modality encoder representations from transformers"
    - "why does a visual question have different answers"
    - "ok vqa a visual question answering benchmark requiring external knowledge"
    - "integrating knowledge and reasoning in image understanding"
    - "deep modular co attention networks for visual question answering"
    - "from recognition to cognition visual commonsense reasoning"
    - "a corpus for reasoning about natural language grounded in photographs"

Connected through hyperedge HE-2 (induced by source paper "representation learning for words and entities"):
  with members:
    - "deep multi view learning to rank"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "new embedded representations and evaluation protocols for inferring transitive relations"
    - "neural collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "ordered memory"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "cooperative learning of disjoint syntax and semantics"
    - "listops a diagnostic dataset for latent tree learning"
    - "improved sentence modeling using suffix bidirectional lstm"
    - "systematic generalization what is required and can it be learned"
    - "deep contextualized word representations"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "the importance of being recurrent for modeling hierarchical structure"

Connected through hyperedge HE-4 (induced by source paper "can neural networks understand monotonicity reasoning"):
  with members:
    - "help a dataset for identifying shortcomings of neural models in monotonicity reasoning"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "can neural networks understand logical entailment"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"

Connected through hyperedge HE-5 (induced by source paper "siamese recurrent networks learn first order logic reasoning and exhibit zero shot compositional generalization"):
  with members:
    - "sentence entailment in compositional distributional semantics"
    - "deep learning a critical appraisal"
    - "can neural networks understand logical entailment"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "learning explanatory rules from noisy data"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "understanding deep learning requires rethinking generalization"
    - "learning continuous semantic representations of symbolic expressions"

Connected through hyperedge HE-6 (induced by source paper "posing fair generalization tasks for natural language inference"):
  with members:
    - "help a dataset for identifying shortcomings of neural models in monotonicity reasoning"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "stress testing neural models of natural language inference with multiply quantified sentences"
    - "analyzing compositionality sensitivity of nli models"
    - "stress test evaluation for natural language inference"
    - "evaluating compositionality in sentence embeddings"

Connected through hyperedge HE-7 (induced by source paper "help a dataset for identifying shortcomings of neural models in monotonicity reasoning"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "hypothesis only baselines in natural language inference"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "stress testing neural models of natural language inference with multiply quantified sentences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"

Connected through hyperedge HE-8 (induced by source paper "learning heuristics for quantified boolean formulas through deep reinforcement learning"):
  with members:
    - "graph representations for higher order logic and theorem proving"
    - "understanding and extending incremental determinization for 2qbf"
    - "learning a sat solver from single bit supervision"
    - "automated theorem proving in intuitionistic propositional logic by deep reinforcement learning"
    - "can neural networks understand logical entailment"
    - "reinforcement learning of theorem proving"
    - "encodings of bounded synthesis"
    - "deep network guided proof search"

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)