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: "a system for induction of oblique decision trees"
  Abstract: This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees.

Connected through hyperedge HE-1 (induced by source paper "towards interpretable deep neural networks an exact transformation to multi class multivariate decision trees"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "an enhanced deep feature representation for person re identification"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "locally constant networks"):
  with members:
    - "towards robust locally linear deep networks"
    - "on the convergence of adam and beyond"
    - "moleculenet a benchmark for molecular machine learning"
    - "densely connected convolutional networks"
    - "predicting the future relevance of research institutions the winning solution of the kdd cup 2016"
    - "deep residual learning for image recognition"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "efficient non greedy optimization of decision trees"

Connected through hyperedge HE-3 (induced by source paper "learning rules first classifiers"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "conditional linear regression"
    - "complexity theoretic limitations on learning halfspaces"
    - "the complexity of learning halfspaces using generalized linear methods"
    - "learning kernel based halfspaces with the zero one loss"

Connected through hyperedge HE-4 (induced by source paper "yasenn explaining neural networks via partitioning activation sequences"):
  with members:
    - "local rule based explanations of black box decision systems"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "interpretable explorable approximations of black box models"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "interpretable convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "enhancing multi class classification of random forest using random vector functional neural network and oblique decision surfaces"):
  with members:
    - "relating cascaded random forests to deep convolutional neural networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "distributed bayesian piecewise sparse linear models"):
  with members:
    - "mobile big data analytics using deep learning and apache spark"
    - "rebuilding factorized information criterion asymptotically accurate marginal likelihood"
    - "alid scalable dominant cluster detection"
    - "mli an api for distributed machine learning"
    - "factorized asymptotic bayesian hidden markov models"

Connected through hyperedge HE-7 (induced by source paper "local decorrelation for improved detection"):
  with members:
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "pedestrian detection with unsupervised multi stage feature learning"

Connected through hyperedge HE-8 (induced by source paper "simultaneous feature and expert selection within mixture of experts"):
  with members:
    - "bayesian hierarchical mixtures of experts"

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.AI(Artificial Intelligence)