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: "patterns for learning with side information"
  Abstract: Supervised, semi-supervised, and unsupervised learning estimate a function given input/output samples. Generalization of the learned function to unseen data can be improved by incorporating side information into learning. Side information are data that are neither from the input space nor from the output space of the function, but include useful information for learning it. In this paper we show that learning with side information subsumes a variety of related approaches, e.g. multi-task learning, multi-view learning and learning using privileged information. Our main contributions are (i) a new perspective that connects these previously isolated approaches, (ii) insights about how these methods incorporate different types of prior knowledge, and hence implement different patterns, (iii) facilitating the application of these methods in novel tasks, as well as (iv) a systematic experimental evaluation of these patterns in two supervised learning tasks.

Connected through hyperedge HE-1 (induced by source paper "incorporating privileged information to unsupervised anomaly detection"):
  with members:
    - "building better detection with privileged information"
    - "extending detection with forensic information"
    - "systematic construction of anomaly detection benchmarks from real data"
    - "learning to transfer privileged information"
    - "privileged information for data clustering"

Connected through hyperedge HE-2 (induced by source paper "building better detection with privileged information"):
  with members:
    - "towards evaluating the robustness of neural networks"
    - "patient driven privacy control through generalized distillation"
    - "deepid3 face recognition with very deep neural networks"
    - "novel feature extraction selection and fusion for effective malware family classification"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "privileged information for data clustering"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-3 (induced by source paper "extending detection with forensic information"):
  with members:
    - "deepid3 face recognition with very deep neural networks"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "privileged information for data clustering"

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)