human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a restricted visual turing test for deep scene and event understanding"
  Abstract: This paper presents a restricted visual Turing test (VTT) for story-line based deep understanding in long-term and multi-camera captured videos. Given a set of videos of a scene (such as a multi-room office, a garden, and a parking lot.) and a sequence of story-line based queries, the task is to provide answers either simply in binary form "true/false" (to a polar query) or in an accurate natural language description (to a non-polar query). Queries, polar or non-polar, consist of view-based queries which can be answered from a particular camera view and scene-centered queries which involves joint inference across different cameras. The story lines are collected to cover spatial, temporal and causal understanding of input videos. The data and queries distinguish our VTT from recently proposed visual question answering in images and video captioning. A vision system is proposed to perform joint video and query parsing which integrates different vision modules, a knowledge base and a query engine. The system provides unified interfaces for different modules so that individual modules can be reconfigured to test a new method. We provide a benchmark dataset and a toolkit for ontology guided story-line query generation which consists of about 93.5 hours videos captured in four different locations and 3,426 queries split into 127 story lines. We also provide a baseline implementation and result analyses.

Connected through hyperedge HE-1 (induced by source paper "large scale pedestrian retrieval competition"):
  with members:
    - "densepose dense human pose estimation in the wild"
    - "learning deep context aware features over body and latent parts for person re identification"

Connected through hyperedge HE-2 (induced by source paper "machine learning with world knowledge the position and survey"):
  with members:
    - "solving hard coreference problems"
    - "a neural knowledge language model"
    - "harnessing deep neural networks with logic rules"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "cross lingual dataless classification for languages with small wikipedia presence"
    - "world knowledge as indirect supervision for document clustering"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "a survey of heterogeneous information network analysis"

Connected through hyperedge HE-3 (induced by source paper "scene centric joint parsing of cross view videos"):
  with members:
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "attribute and or grammar for joint parsing of human attributes part and pose"
    - "an efficient minibatch acceptance test for metropolis hastings"
    - "visual7w grounded question answering in images"
    - "vqa visual question answering"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "cross view action modeling learning and recognition"

Connected through hyperedge HE-4 (induced by source paper "a large scale distributed video parsing and evaluation platform"):
  with members:
    - "context matters refining object detection in video with recurrent neural networks"
    - "person re identification by discriminative selection in video ranking"

Question: Please tell me which class the center node 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)