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 "multi representational learning for offline signature verification using multi loss snapshot ensemble of cnns"):
  Abstract: Offline Signature Verification (OSV) is a challenging pattern recognition task, especially in presence of skilled forgeries that are not available during training. This study aims to tackle its challenges and meet the substantial need for generalization for OSV by examining different loss functions for Convolutional Neural Network (CNN). We adopt our new approach to OSV by asking two questions: 1. which classification loss provides more generalization for feature learning in OSV? , and 2. How integration of different losses into a unified multi-loss function lead to an improved learning framework? These questions are studied based on analysis of three loss functions, including cross entropy, Cauchy-Schwarz divergence, and hinge loss. According to complementary features of these losses, we combine them into a dynamic multi-loss function and propose a novel ensemble framework for simultaneous use of them in CNN. Our proposed Multi-Loss Snapshot Ensemble (MLSE) consists of several sequential trials. In each trial, a dominant loss function is selected from the multi-loss set, and the remaining losses act as a regularizer. Different trials learn diverse representations for each input based on signature identification task. This multi-representation set is then employed for the verification task. An ensemble of SVMs is trained on these representations, and their decisions are finally combined according to the selection of most generalizable SVM for each user. We conducted two sets of experiments based on two different protocols of OSV, i.e., writer-dependent and writer-independent on three signature datasets: GPDS-Synthetic, MCYT, and UT-SIG. Based on the writer-dependent OSV protocol, we achieved substantial improvements over the best EERs in the literature. The results of the second set of experiments also confirmed the robustness to the arrival of new users enrolled in the OSV system.

Includes members:
  Member "active transfer learning for persian offline signature verification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning representations from persian handwriting for offline signature verification a deep transfer learning approach")
  Member "learning representations from persian handwriting for offline signature verification a deep transfer learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "active transfer learning for persian offline signature verification")
  Member "a writer independent approach for offline signature verification using deep convolutional neural networks features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "biometric recognition using deep learning a survey")
  Member "fixed sized representation learning from offline handwritten signatures of different sizes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning for fast classifier adaptation to new users of signature verification systems")
  Member "using mode connectivity for loss landscape analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "loss landscape sightseeing with multi point optimization")
  Member "a comprehensive study of sparse representation techniques for offline signature verification":
    also connected through other hyperedges:
      (none)
  Member "offline handwritten signature verification literature review":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meta learning for fast classifier adaptation to new users of signature verification systems")
  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")

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)