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 task collaborative network for joint referring expression comprehension and segmentation"):
  Abstract: Referring expression comprehension (REC) and segmentation (RES) are two highly-related tasks, which both aim at identifying the referent according to a natural language expression. In this paper, we propose a novel Multi-task Collaborative Network (MCN) to achieve a joint learning of REC and RES for the first time. In MCN, RES can help REC to achieve better language-vision alignment, while REC can help RES to better locate the referent. In addition, we address a key challenge in this multi-task setup, i.e., the prediction conflict, with two innovative designs namely, Consistency Energy Maximization (CEM) and Adaptive Soft Non-Located Suppression (ASNLS). Specifically, CEM enables REC and RES to focus on similar visual regions by maximizing the consistency energy between two tasks. ASNLS supresses the response of unrelated regions in RES based on the prediction of REC. To validate our model, we conduct extensive experiments on three benchmark datasets of REC and RES, i.e., RefCOCO, RefCOCO+ and RefCOCOg. The experimental results report the significant performance gains of MCN over all existing methods, i.e., up to +7.13% for REC and +11.50% for RES over SOTA, which well confirm the validity of our model for joint REC and RES learning.

Includes members:
  Member "a real time global inference network for one stage referring expression comprehension":
    also connected through other hyperedges:
      (none)
  Member "retinamask learning to predict masks improves state of the art single shot detection for free":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the evaluation and real world usage scenarios of deep vessel segmentation for retinography")
  Member "zero shot grounding of objects from natural language queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scanrefer 3d object localization in rgb d scans using natural language")
  Member "cross modal self attention network for referring image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cops ref a new dataset and task on compositional referring expression comprehension")
  Member "yolact real time instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pointins point based instance segmentation")
  Member "dynamic multimodal instance segmentation guided by natural language queries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dual convolutional lstm network for referring image segmentation")
  Member "neighbourhood watch referring expression comprehension via language guided graph attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cops ref a new dataset and task on compositional referring expression comprehension")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")

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)