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 "recal reuse of established cnn classifer apropos unsupervised learning paradigm"):
  Abstract: Recently, clustering with deep network framework has attracted attention of several researchers in the computer vision community. Deep framework gains extensive attention due to its efficiency and scalability towards large-scale and high-dimensional data. In this paper, we transform supervised CNN classifier architecture into an unsupervised clustering model, called RECAL, which jointly learns discriminative embedding subspace and cluster labels. RECAL is made up of feature extraction layers which are convolutional, followed by unsupervised classifier layers which is fully connected. A multinomial logistic regression function (softmax) stacked on top of classifier layers. We train this network using stochastic gradient descent (SGD) optimizer. However, the successful implementation of our model is revolved around the design of loss function. Our loss function uses the heuristics that true partitioning entails lower entropy given that the class distribution is not heavily skewed. This is a trade-off between the situations of "skewed distribution" and "low-entropy". To handle this, we have proposed classification entropy and class entropy which are the two components of our loss function. In this approach, size of the mini-batch should be kept high. Experimental results indicate the consistent and competitive behavior of our model for clustering well-known digit, multi-viewed object and face datasets. Morever, we use this model to generate unsupervised patch segmentation for multi-spectral LISS-IV images. We observe that it is able to distinguish built-up area, wet land, vegetation and waterbody from the underlying scene.

Includes members:
  Member "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised domain adaptation via structurally regularized deep clustering")
  Member "joint unsupervised learning of deep representations and image clusters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "learning a task specific deep architecture for clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised person re identification by deep asymmetric metric embedding")
  Member "unsupervised deep embedding for clustering analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "memory based graph networks")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "graph degree linkage agglomerative clustering on a directed graph":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "clustering by directly disentangling latent space")

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)