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: "action2activity recognizing complex activities from sensor data"
  Abstract: As compared to simple actions, activities are much more complex, but semantically consistent with a human's real life. Techniques for action recognition from sensor generated data are mature. However, there has been relatively little work on bridging the gap between actions and activities. To this end, this paper presents a novel approach for complex activity recognition comprising of two components. The first component is temporal pattern mining, which provides a mid-level feature representation for activities, encodes temporal relatedness among actions, and captures the intrinsic properties of activities. The second component is adaptive Multi-Task Learning, which captures relatedness among activities and selects discriminant features. Extensive experiments on a real-world dataset demonstrate the effectiveness of our work.

Connected through hyperedge HE-1 (induced by source paper "local directional relation pattern for unconstrained and robust face retrieval"):
  with members:
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "a comprehensive survey on pose invariant face recognition"
    - "the megaface benchmark 1 million faces for recognition at scale"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "a general framework for complex network based image segmentation"):
  with members:
    - "segmentation of large images based on super pixels and community detection in graphs"
    - "qualitative comparison of community detection algorithms"
    - "on accuracy of community structure discovery algorithms"

Connected through hyperedge HE-3 (induced by source paper "using compressed suffix arrays for a compact representation of temporal graphs"):
  with members:
    - "compact trip representation over networks"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "exploring the structure and function of temporal networks with dynamic graphlets"
    - "windows into relational events data structures for contiguous subsequences of edges"
    - "efficient snapshot retrieval over historical graph data"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-4 (induced by source paper "copy move forgery detection based on convolutional kernel network"):
  with members:
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "predicting urban water quality with ubiquitous data"
    - "convolutional oriented boundaries"
    - "identity mappings in deep residual networks"
    - "convolutional patch representations for image retrieval an unsupervised approach"
    - "holistically nested edge detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "efficient eye typing with 9 direction gaze estimation"):
  with members:
    - "eye tracking for everyone"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "appearance based gaze estimation in the wild"

Connected through hyperedge HE-6 (induced by source paper "predicting urban water quality with ubiquitous data"):
  with members:
    - "a survey on multi view learning"
    - "a convex formulation for learning task relationships in multi task learning"

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)