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 structural kernels for natural language processing"
  Abstract: Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods.

Connected through hyperedge HE-1 (induced by source paper "sentiment analysis with genetically evolved gaussian kernels"):
  with members:
    - "leveraging large amounts of weakly supervised data for multi language sentiment classification"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "evolution of covariance functions for gaussian process regression using genetic programming"
    - "genetic programming for kernel based learning with co evolving subsets selection"

Connected through hyperedge HE-2 (induced by source paper "stochastic learning of nonstationary kernels for natural language modeling"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "learning to skim text"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "effective approaches to attention based neural machine translation"
    - "classifying relations by ranking with convolutional neural networks"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "task oriented learning of word embeddings for semantic relation classification"
    - "semantic relation classification via convolutional neural networks with simple negative sampling"

Connected through hyperedge HE-3 (induced by source paper "kernelized hashcode representations for relation extraction"):
  with members:
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deep learning for extracting protein protein interactions from biomedical literature"
    - "deep variational information bottleneck"
    - "a survey on learning to hash"
    - "orthogonal random features"
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "adaptive estimation of shannon entropy"
    - "extracting biomolecular interactions using semantic parsing of biomedical text"

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)