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: "learning distributed word representations for natural logic reasoning"
  Abstract: Natural logic offers a powerful relational conception of meaning that is a natural counterpart to distributed semantic representations, which have proven valuable in a wide range of sophisticated language tasks. However, it remains an open question whether it is possible to train distributed representations to support the rich, diverse logical reasoning captured by natural logic. We address this question using two neural network-based models for learning embeddings: plain neural networks and neural tensor networks. Our experiments evaluate the models' ability to learn the basic algebra of natural logic relations from simulated data and from the WordNet noun graph. The overall positive results are promising for the future of learned distributed representations in the applied modeling of logical semantics.

Connected through hyperedge HE-1 (induced by source paper "neural models of factuality"):
  with members:
    - "how transferable are neural networks in nlp applications"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"
    - "learning to execute"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (induced by source paper "ontology reasoning with deep neural networks"):
  with members:
    - "lifted rule injection for relation embeddings"
    - "end to end memory networks"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "holographic embeddings of knowledge graphs"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "on the effective use of pretraining for natural language inference"):
  with members:
    - "long short term memory networks for machine reading"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"
    - "semi supervised sequence learning"
    - "learning natural language inference with lstm"
    - "reasoning about entailment with neural attention"
    - "a large annotated corpus for learning natural language inference"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (induced by source paper "semi supervised vocabulary informed learning"):
  with members:
    - "transductive multi view zero shot learning"
    - "towards open world recognition"
    - "evaluation of output embeddings for fine grained image classification"
    - "prediction of search targets from fixations in open world settings"
    - "zero shot recognition with unreliable attributes"
    - "return of the devil in the details delving deep into convolutional nets"
    - "a unified semantic embedding relating taxonomies and attributes"
    - "zero shot learning by convex combination of semantic embeddings"

Connected through hyperedge HE-5 (induced by source paper "path based vs distributional information in recognizing lexical semantic relations"):
  with members:
    - "nine features in a random forest to learn taxonomical semantic relations"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "computing lexical contrast"
    - "distributed representations of words and phrases and their compositionality"

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)