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: "ropad robust presentation attack detection through unsupervised adversarial invariance"
  Abstract: For enterprise, personal and societal applications, there is now an increasing demand for automated authentication of identity from images using computer vision. However, current authentication technologies are still vulnerable to presentation attacks. We present RoPAD, an end-to-end deep learning model for presentation attack detection that employs unsupervised adversarial invariance to ignore visual distractors in images for increased robustness and reduced overfitting. Experiments show that the proposed framework exhibits state-of-the-art performance on presentation attack detection on several benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "use of a capsule network to detect fake images and videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "fsgan subject agnostic face swapping and reenactment"
    - "deferred neural rendering image synthesis using neural textures"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "domain adaptation in multi channel autoencoder based features for robust face anti spoofing"
    - "deep pixel wise binary supervision for face presentation attack detection"

Connected through hyperedge HE-2 (induced by source paper "discovery and separation of features for invariant representation learning"):
  with members:
    - "unified adversarial invariance"
    - "invariant representations through adversarial forgetting"
    - "exact rate distortion in autoencoders via echo noise"
    - "niesr nuisance invariant end to end speech recognition"
    - "invariant representations without adversarial training"
    - "unsupervised adversarial invariance"
    - "information constraints on auto encoding variational bayes"
    - "emergence of invariance and disentanglement in deep representations"

Connected through hyperedge HE-3 (induced by source paper "invariant representations through adversarial forgetting"):
  with members:
    - "unified adversarial invariance"
    - "niesr nuisance invariant end to end speech recognition"
    - "invariant representations without adversarial training"
    - "unsupervised adversarial invariance"
    - "information constraints on auto encoding variational bayes"
    - "emergence of invariance and disentanglement in deep representations"
    - "dynamic routing between capsules"
    - "controllable invariance through adversarial feature learning"

Connected through hyperedge HE-4 (induced by source paper "niesr nuisance invariant end to end speech recognition"):
  with members:
    - "unified adversarial invariance"
    - "extracting domain invariant features by unsupervised learning for robust automatic speech recognition"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "very deep convolutional networks for end to end speech recognition"
    - "invariant representations for noisy speech recognition"
    - "listen attend and spell"
    - "attention based models for speech recognition"
    - "neural machine translation by jointly learning to align and translate"

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)