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 compact network learning model for distribution regression"
  Abstract: Despite the superior performance of deep learning in many applications, challenges remain in the area of regression on function spaces. In particular, neural networks are unable to encode function inputs compactly as each node encodes just a real value. We propose a novel idea to address this shortcoming: to encode an entire function in a single network node. To that end, we design a compact network representation that encodes and propagates functions in single nodes for the distribution regression task. Our proposed Distribution Regression Network (DRN) achieves higher prediction accuracies while being much more compact and uses fewer parameters than traditional neural networks.

Connected through hyperedge HE-1 (induced by source paper "enhancing transformation based defenses using a distribution classifier"):
  with members:
    - "defending adversarial attacks by correcting logits"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial patch"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "deflecting adversarial attacks with pixel deflection"
    - "black box adversarial attacks with limited queries and information"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-2 (induced by source paper "a weakly supervised learning based clustering framework"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "predicting time varying distributions with limited training data"
    - "deep sets"
    - "predict and constrain modeling cardinality in deep structured prediction"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deepsetnet predicting sets with deep neural networks"
    - "group equivariant convolutional networks"
    - "joint unsupervised learning of deep representations and image clusters"

Connected through hyperedge HE-3 (induced by source paper "theoretical and experimental analysis on the generalizability of distribution regression network"):
  with members:
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-4 (induced by source paper "predicting time varying distributions with limited training data"):
  with members:
    - "decaf a deep convolutional activation feature for generic visual recognition"

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)