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: "efficiently inducing features of conditional random fields"
  Abstract: Conditional Random Fields (CRFs) are undirected graphical models, a special case of which correspond to conditionally-trained finite state machines. A key advantage of these models is their great flexibility to include a wide array of overlapping, multi-granularity, non-independent features of the input. In face of this freedom, an important question that remains is, what features should be used? This paper presents a feature induction method for CRFs. Founded on the principle of constructing only those feature conjunctions that significantly increase log-likelihood, the approach is based on that of Della Pietra et al [1997], but altered to work with conditional rather than joint probabilities, and with additional modifications for providing tractability specifically for a sequence model. In comparison with traditional approaches, automated feature induction offers both improved accuracy and more than an order of magnitude reduction in feature count; it enables the use of richer, higher-order Markov models, and offers more freedom to liberally guess about which atomic features may be relevant to a task. The induction method applies to linear-chain CRFs, as well as to more arbitrary CRF structures, also known as Relational Markov Networks [Taskar & Koller, 2002]. We present experimental results on a named entity extraction task.

Connected through hyperedge HE-1 (induced by source paper "geometric matrix completion with deep conditional random fields"):
  with members:
    - "regularizing autoencoder based matrix completion models via manifold learning"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "geometric deep learning going beyond euclidean data"
    - "deep learning markov random field for semantic segmentation"
    - "robust on line matrix completion on graphs"
    - "semi supervised classification with graph convolutional networks"
    - "a neural autoregressive approach to collaborative filtering"
    - "hybrid recommender system based on autoencoders"

Connected through hyperedge HE-2 (induced by source paper "character feature engineering for japanese word segmentation"):
  with members:
    - "chinese word segmentation another decade review 2007 2017"
    - "long short term memory for japanese word segmentation"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation learning for chinese"
    - "mostly unsupervised statistical segmentation of japanese kanji sequences"
    - "representing text chunks"

Connected through hyperedge HE-3 (induced by source paper "log linear models independence structure comparison"):
  with members:
    - "blankets joint posterior score for learning markov network structures"
    - "the grow shrink strategy for learning markov network structures constrained by context specific independences"
    - "the ibmap approach for markov network structure learning"
    - "context specific independence in bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "blankets joint posterior score for learning markov network structures"):
  with members:
    - "the libra toolkit for probabilistic models"
    - "a survey on independence based markov networks learning"
    - "the ibmap approach for markov network structure learning"
    - "constrained approximate maximum entropy learning of markov random fields"

Connected through hyperedge HE-5 (induced by source paper "de identification in practice"):
  with members:
    - "de identification of patient notes with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "texture modelling with nested high order markov gibbs random fields"):
  with members:
    - "texture modeling with convolutional spike and slab rbms and deep extensions"

Connected through hyperedge HE-7 (induced by source paper "latent hierarchical model for activity recognition"):
  with members:
    - "learning human activities and object affordances from rgb d videos"

Connected through hyperedge HE-8 (induced by source paper "contrastive feature induction for efficient structure learning of conditional random fields"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "discriminative probabilistic models for relational data"
    - "template based inference in symmetric relational markov random fields"
    - "a generalized mean field algorithm for variational inference in exponential families"

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)