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: "can recursive neural tensor networks learn logical reasoning"
  Abstract: Recursive neural network models and their accompanying vector representations for words have seen success in an array of increasingly semantically sophisticated tasks, but almost nothing is known about their ability to accurately capture the aspects of linguistic meaning that are necessary for interpretation or reasoning. To evaluate this, I train a recursive model on a new corpus of constructed examples of logical reasoning in short sentences, like the inference of "some animal walks" from "some dog walks" or "some cat walks," given that dogs and cats are animals. This model learns representations that generalize well to new types of reasoning pattern in all but a few cases, a result which is promising for the ability of learned representation models to capture logical reasoning.

Connected through hyperedge HE-1 (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-2 (induced by source paper "dynamic neural program embedding for program repair"):
  with members:
    - "data driven feedback generation for introductory programming exercises"
    - "making neural programming architectures generalize via recursion"
    - "automated correction for syntax errors in programming assignments using recurrent neural networks"
    - "sk_p a neural program corrector for moocs"
    - "neural programmer interpreters"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks over tree structures for programming language processing"
    - "learning to discover efficient mathematical identities"

Connected through hyperedge HE-3 (induced by source paper "deep learning for ontology reasoning"):
  with members:
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "pycuda and pyopencl a scripting based approach to gpu run time code generation"

Connected through hyperedge HE-4 (induced by source paper "simpler context dependent logical forms via model projections"):
  with members:
    - "neural programmer inducing latent programs with gradient descent"
    - "compositional semantic parsing on semi structured tables"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "traversing knowledge graphs in vector space"
    - "question answering with subgraph embeddings"
    - "lambda dependency based compositional semantics"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

Connected through hyperedge HE-5 (induced by source paper "probabilistic reasoning via deep learning neural association models"):
  with members:
    - "the goldilocks principle reading children s books with explicit memory representations"
    - "a large annotated corpus for learning natural language inference"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "efficient estimation of word representations in vector space"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "when are tree structures necessary for deep learning of representations"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "improved semantic representations from tree structured long short term memory networks"
    - "tree structured composition in neural networks without tree structured architectures"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "distributed representations of sentences and documents"
    - "recursive neural networks can learn logical semantics"
    - "neural machine translation by jointly learning to align and translate"
    - "grammar as a foreign language"

Connected through hyperedge HE-7 (induced by source paper "traversing knowledge graphs in vector space"):
  with members:
    - "compositional vector space models for knowledge base completion"
    - "deepwalk online learning of social representations"
    - "recursive neural networks can learn logical semantics"
    - "question answering with subgraph embeddings"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "theano new features and speed improvements"

Connected through hyperedge HE-8 (induced by source paper "learning program embeddings to propagate feedback on student code"):
  with members:
    - "neural turing machines"
    - "learning to discover efficient mathematical identities"

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)