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 "a new evaluation protocol and benchmarking results for extendable cross media retrieval"):
  Abstract: This paper proposes a new evaluation protocol for cross-media retrieval which better fits the real-word applications. Both image-text and text-image retrieval modes are considered. Traditionally, class labels in the training and testing sets are identical. That is, it is usually assumed that the query falls into some pre-defined classes. However, in practice, the content of a query image/text may vary extensively, and the retrieval system does not necessarily know in advance the class label of a query. Considering the inconsistency between the real-world applications and laboratory assumptions, we think that the existing protocol that works under identical train/test classes can be modified and improved. #R##N#This work is dedicated to addressing this problem by considering the protocol under an extendable scenario, \ie, the training and testing classes do not overlap. We provide extensive benchmarking results obtained by the existing protocol and the proposed new protocol on several commonly used datasets. We demonstrate a noticeable performance drop when the testing classes are unseen during training. Additionally, a trivial solution, \ie, directly using the predicted class label for cross-media retrieval, is tested. We show that the trivial solution is very competitive in traditional non-extendable retrieval, but becomes less so under the new settings. The train/test split, evaluation code, and benchmarking results are publicly available on our website.

Includes members:
  Member "image text multi modal representation learning by adversarial backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "do cross modal systems leverage semantic relationships")
  Member "how should we evaluate supervised hashing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semantic cluster unary loss for efficient deep hashing")
  Member "person re identification past present and future":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "deep cross modal hashing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "sift meets cnn a decade survey of instance retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "matching neuromorphic events and color images via adversarial learning")
  Member "learning deep structure preserving image text embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "multimodal convolutional neural networks for matching image and sentence":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "imram iterative matching with recurrent attention memory for cross modal image text retrieval")
  Member "unifying visual semantic embeddings with multimodal neural language models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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)