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: "signer independent fingerspelling recognition with deep neural network adaptation"
  Abstract: We study the problem of recognition of fingerspelled letter sequences in American Sign Language in a signer-independent setting. Fingerspelled sequences are both challenging and important to recognize, as they are used for many content words such as proper nouns and technical terms. Previous work has shown that it is possible to achieve almost 90% accuracies on fingerspelling recognition in a signer-dependent setting. However, the more realistic signer-independent setting presents challenges due to significant variations among signers, coupled with the dearth of available training data. We investigate this problem with approaches inspired by automatic speech recognition. We start with the best-performing approaches from prior work, based on tandem models and segmental conditional random fields (SCRFs), with features based on deep neural network (DNN) classifiers of letters and phonological features. Using DNN adaptation, we find that it is possible to bridge a large part of the gap between signer-dependent and signer-independent performance. Using only about 115 transcribed words for adaptation from the target signer, we obtain letter accuracies of up to 82.7% with frame-level adaptation labels and 69.7% with only word labels.

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"
    - "lexicon free fingerspelling recognition from video data models and signer 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"
    - "lexicon free fingerspelling recognition from video data models and signer 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:
    - "lexicon free fingerspelling recognition from video data models and signer 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"

Connected through hyperedge HE-4 (induced by source paper "lexicon free fingerspelling recognition from video data models and signer adaptation"):
  with members:
    - "segmental recurrent neural networks for end to end speech recognition"
    - "theano a python framework for fast computation of mathematical expressions"
    - "discriminative segmental cascades for feature rich phone recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "american sign language fingerspelling recognition from video methods for unrestricted recognition and signer independence"):
  with members:
    - "discriminative segmental cascades for feature rich phone recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "frustratingly easy domain adaptation"

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)