human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "facial landmark detection using structured output deep neural networks"):
  Abstract: Facial landmark detection is an important step for many perception tasks such as face recognition and facial analysis. Regression-based methods have shown a large success. In particular, deep neural networks (DNN) has demonstrated a strong capability to model the high non-linearity between the face image and the face shape. In this paper, we tackle this task as a structured output problem, where we exploit the strong dependencies that lie between the outputs. Beside learning a regression mapping function from the input to the output, we learn, in an unsupervised way, the inter-dependencies between the outputs. For this, we propose a generic regression framework for structured output problems. Our framework allows a successful incorporation of learning the output structure into DNN using the pre-training trick. We apply our method on a facial landmark detection task, where the output is strongly structured. We evaluate our DNN, named Input/Output Deep Architecture (IODA), on two public challenging datasets: LFPW and HELEN. We show that IODA outperforms traditional deep architectures.

Includes members:
  Member "fcns in the wild pixel level adversarial and constraint based adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "structured prediction energy networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differentiable fixed point iteration layer")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "correlational neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards a general model of knowledge for facial analysis by multi source transfer learning")
  Member "learning deconvolution network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "deep structured output learning for unconstrained text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scene text recognition via transformer")
  Member "deep visual semantic alignments for generating image descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "sequence to sequence learning with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

Question: Please tell me which class the source paper 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)