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 "contrastive adaptation network for unsupervised domain adaptation"):
  Abstract: Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain. Previous methods minimize the domain discrepancy neglecting the class information, which may lead to misalignment and poor generalization performance. To address this issue, this paper proposes Contrastive Adaptation Network (CAN) optimizing a new metric which explicitly models the intra-class domain discrepancy and the inter-class domain discrepancy. We design an alternating update strategy for training CAN in an end-to-end manner. Experiments on two real-world benchmarks Office-31 and VisDA-2017 demonstrate that CAN performs favorably against the state-of-the-art methods and produces more discriminative features.

Includes members:
  Member "searching for a robust neural architecture in four gpu hours":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning architectures for binary networks")
  Member "shakeout a new approach to regularized deep neural network training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "asymptotic soft filter pruning for deep convolutional neural networks")
  Member "visual domain adaptation with manifold embedded distribution alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "impact of imagenet model selection on domain adaptation")
  Member "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "multi adversarial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "in defense of the triplet loss for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "cycada cycle consistent adversarial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a novel framework for selection of gans for an application")

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.CV(Computer Vision and Pattern Recognition)