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: "multidimensional classification of hippocampal shape features discriminates alzheimer s disease and mild cognitive impairment from normal aging"
  Abstract: We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hippocampi, which are segmented from magnetic resonance images (MRI) using a fully automatic method that we previously developed. SPHARM coefficients are used as features in a classification procedure based on support vector machines (SVM). The most relevant features for classification are selected using a bagging strategy. We evaluate the accuracy of our method in a group of 23 patients with AD (10 males, 13 females, age+/-standard-deviation (SD)=73+/-6 years, mini-mental score (MMS)=24.4+/-2.8), 23 patients with amnestic MCI (10 males, 13 females, age+/-SD=74+/-8 years, MMS=27.3+/-1.4) and 25 elderly healthy controls (13 males, 12 females, age+/-SD=64+/-8 years), using leave-one-out cross-validation. For AD vs controls, we obtain a correct classification rate of 94%, a sensitivity of 96%, and a specificity of 92%. For MCI vs controls, we obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of 84%. This accuracy is superior to that of hippocampal volumetry and is comparable to recently published SVM-based whole-brain classification methods, which relied on a different strategy. This new method may become a useful tool to assist in the diagnosis of Alzheimer's disease.

Connected through hyperedge HE-1 (induced by source paper "a wide and deep neural network for survival analysis from anatomical shape and tabular clinical data"):
  with members:
    - "deep multi structural shape analysis application to neuroanatomy"
    - "tune a research platform for distributed model selection and training"
    - "wide deep learning for recommender systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "deep ensemble learning for alzheimers disease classification"):
  with members:
    - "stacking classifiers for anti spam filtering of e mail"

Connected through hyperedge HE-3 (induced by source paper "neuroimaging modality fusion in alzheimer s classification using convolutional neural networks"):
  with members:
    - "alzheimer s disease diagnostics by adaptation of 3d convolutional network"
    - "classification of alzheimer s disease using fmri data and deep learning convolutional neural networks"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "deep multi structural shape analysis application to neuroanatomy"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "a survey on deep learning in medical image analysis"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "a taxonomy and library for visualizing learned features in convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep shape analysis on abdominal organs for diabetes prediction"):
  with members:
    - "gaussian process uncertainty in age estimation as a measure of brain abnormality"
    - "deep multi structural shape analysis application to neuroanatomy"

Connected through hyperedge HE-6 (induced by source paper "deep ordinal ranking for multi category diagnosis of alzheimer s disease using hippocampal mri data"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "alzheimer s disease diagnostics by a deeply supervised adaptable 3d convolutional network"):
  with members:
    - "deep learning of part based representation of data using sparse autoencoders with nonnegativity constraints"
    - "learning transferable features with deep adaptation networks"
    - "net2net accelerating learning via knowledge transfer"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "how transferable are features in deep neural networks"
    - "winner take all autoencoders"
    - "adadelta an adaptive learning rate method"
    - "theano new features and speed improvements"

Connected through hyperedge HE-8 (induced by source paper "alzheimer s disease diagnostics by adaptation of 3d convolutional network"):
  with members:
    - "classification of alzheimer s disease using fmri data and deep learning convolutional neural networks"
    - "learning transferable features with deep adaptation networks"
    - "net2net accelerating learning via knowledge transfer"
    - "how transferable are features in deep neural networks"
    - "winner take all autoencoders"
    - "adadelta an adaptive learning rate method"
    - "theano new features and speed improvements"

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)