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: "lexicon free fingerspelling recognition from video data models and signer adaptation"
  Abstract: We study the problem of recognizing video sequences of fingerspelled letters in American Sign Language (ASL). Fingerspelling comprises a significant but relatively understudied part of ASL. Recognizing fingerspelling is challenging for a number of reasons: It involves quick, small motions that are often highly coarticulated; it exhibits significant variation between signers; and there has been a dearth of continuous fingerspelling data collected. In this work we collect and annotate a new data set of continuous fingerspelling videos, compare several types of recognizers, and explore the problem of signer variation. Our best-performing models are segmental (semi-Markov) conditional random fields using deep neural network-based features. In the signer-dependent setting, our recognizers achieve up to about 92% letter accuracy. The multi-signer setting is much more challenging, but with neural network adaptation we achieve up to 83% letter accuracies in this setting.

Connected through hyperedge HE-1 (induced by source paper "fingerspelling recognition in the wild with iterative visual attention"):
  with members:
    - "american sign language fingerspelling recognition in the wild"
    - "video based sign language recognition without temporal segmentation"
    - "multitask training with unlabeled data for end to end sign language fingerspelling recognition"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "diversified visual attention networks for fine grained object classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "signer independent fingerspelling recognition with deep neural network adaptation"

Connected through hyperedge HE-2 (induced by source paper "american sign language fingerspelling recognition in the wild"):
  with members:
    - "multitask training with unlabeled data for end to end sign language fingerspelling recognition"
    - "signer independent fingerspelling recognition with deep neural network adaptation"
    - "fast r cnn"
    - "learning to track for spatio temporal action localization"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "grammar as a foreign language"

Connected through hyperedge HE-3 (induced by source paper "multitask training with unlabeled data for end to end sign language fingerspelling recognition"):
  with members:
    - "signer independent fingerspelling recognition with deep neural network adaptation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "grammar as a foreign language"
    - "some improvements on deep convolutional neural network based image classification"

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.CL(Computation and Language)