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: "generating user friendly explanations for loan denials using gans"
  Abstract: Financial decisions impact our lives, and thus everyone from the regulator to the consumer is interested in fair, sound, and explainable decisions. There is increasing competitive desire and regulatory incentive to deploy AI mindfully within financial services. An important mechanism towards that end is to explain AI decisions to various stakeholders. State-of-the-art explainable AI systems mostly serve AI engineers and offer little to no value to business decision makers, customers, and other stakeholders. Towards addressing this gap, in this work we consider the scenario of explaining loan denials. We build the first-of-its-kind dataset that is representative of loan-applicant friendly explanations. We design a novel Generative Adversarial Network (GAN) that can accommodate smaller datasets, to generate user-friendly textual explanations. We demonstrate how our system can also generate explanations serving different purposes: those that help educate the loan applicants, or help them take appropriate action towards a future approval.

Connected through hyperedge HE-1 (induced by source paper "a new codebook design for analog beamforming in millimeter wave communication"):
  with members:
    - "investigating robustness and interpretability of link prediction via adversarial modifications"
    - "partition pruning parallelization aware pruning for deep neural networks"
    - "age of information in multiple sensing"
    - "on the age of information in erasure channels with feedback"
    - "hybrid precoder and combiner design with low resolution phase shifters in mmwave mimo systems"
    - "embedding multimodal relational data for knowledge base completion"
    - "on the performance of mrc receiver with unknown timing mismatch a large scale analysis"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"

Connected through hyperedge HE-2 (induced by source paper "maximal adversarial perturbations for obfuscation hiding certain attributes while preserving rest"):
  with members:
    - "distributed generation of privacy preserving data with user customization"
    - "fair lending needs explainable models for responsible recommendation"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "privacy protective gan for face de identification"
    - "an interpretable model with globally consistent explanations for credit risk"
    - "balanced datasets are not enough estimating and mitigating gender bias in deep image representations"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "synthesizing robust adversarial examples"

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)