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 "what makes training multi modal networks hard"):
  Abstract: Consider end-to-end training of a multi-modal vs. a single-modal network on a task with multiple input modalities: the multi-modal network receives more information, so it should match or outperform its single-modal counterpart. In our experiments, however, we observe the opposite: the best single-modal network always outperforms the multi-modal network. This observation is consistent across different combinations of modalities and on different tasks and benchmarks. #R##N#This paper identifies two main causes for this performance drop: first, multi-modal networks are often prone to overfitting due to increased capacity. Second, different modalities overfit and generalize at different rates, so training them jointly with a single optimization strategy is sub-optimal. We address these two problems with a technique we call Gradient Blending, which computes an optimal blend of modalities based on their overfitting behavior. We demonstrate that Gradient Blending outperforms widely-used baselines for avoiding overfitting and achieves state-of-the-art accuracy on various tasks including fine-grained sport classification, human action recognition, and acoustic event detection.

Includes members:
  Member "deep hough voting for 3d object detection in point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds")
  Member "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "duration robust weakly supervised sound event detection")
  Member "the sound of pixels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "cooperative learning of audio and video models from self supervised synchronization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "audio visual scene analysis with self supervised multisensory features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi modal self supervision from generalized data transformations")
  Member "learning spatio temporal representation with pseudo 3d residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos")
  Member "quo vadis action recognition a new model and the kinetics dataset":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "a closer look at spatiotemporal convolutions for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "probabilistic future prediction for video scene understanding")

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)