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 "cross paced representation learning with partial curricula for sketch based image retrieval"):
  Abstract: In this paper, we address the problem of learning robust cross-domain representations for sketch-based image retrieval (SBIR). While, most SBIR approaches focus on extracting low- and mid-level descriptors for direct feature matching, recent works have shown the benefit of learning coupled feature representations to describe data from two related sources. However, cross-domain representation learning methods are typically cast into non-convex minimization problems that are difficult to optimize, leading to unsatisfactory performance. Inspired by self-paced learning (SPL), a learning methodology designed to overcome convergence issues related to local optima by exploiting the samples in a meaningful order (i.e., easy to hard), we introduce the cross-paced partial curriculum learning (CPPCL) framework. Compared with existing SPL methods which only consider a single modality and cannot deal with prior knowledge, CPPCL is specifically designed to assess the learning pace by jointly handling data from dual sources and modality-specific prior information provided in the form of partial curricula. In addition, thanks to the learned dictionaries, we demonstrate that the proposed CPPCL embeds robust coupled representations for SBIR. Our approach is extensively evaluated on four publicly available datasets (i.e., CUFS, Flickr15K, QueenMary SBIR, and TU-Berlin Extension datasets), showing superior performance over competing SBIR methods.

Includes members:
  Member "learning deep structured multi scale features using attention gated crfs for contour prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised learning of camera pose with compositional re estimation")
  Member "learning cross modal deep representations for robust pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how much real data do we actually need analyzing object detection performance using synthetic and real data")
  Member "deep sketch hashing fast free hand sketch based image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a network pruning network approach to deep model compression")
  Member "multi scale continuous crfs as sequential deep networks for monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields")
  Member "sketch based 3d shape retrieval using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval")
  Member "learning deep representations of appearance and motion for anomalous event detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self trained deep ordinal regression for end to end video anomaly detection")
  Member "sketch a net that beats humans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning robust representations via multi view information bottleneck")

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)