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: "early mfcc and hpcp fusion for robust cover song identification"
  Abstract: While most schemes for automatic cover song identification have focused on note-based features such as HPCP and chord profiles, a few recent papers surprisingly showed that local self-similarities of MFCC-based features also have classification power for this task. Since MFCC and HPCP capture complementary information, we design an unsupervised algorithm that combines normalized, beat-synchronous blocks of these features using cross-similarity fusion before attempting to locally align a pair of songs. As an added bonus, our scheme naturally incorporates structural information in each song to fill in alignment gaps where both feature sets fail. We show a striking jump in performance over MFCC and HPCP alone, achieving a state of the art mean reciprocal rank of 0.87 on the Covers80 dataset. We also introduce a new medium-sized hand designed benchmark dataset called "Covers 1000," which consists of 395 cliques of cover songs for a total of 1000 songs, and we show that our algorithm achieves an MRR of 0.9 on this dataset for the first correctly identified song in a clique. We provide the precomputed HPCP and MFCC features, as well as beat intervals, for all songs in the Covers 1000 dataset for use in further research.

Connected through hyperedge HE-1 (induced by source paper "accurate and scalable version identification using musically motivated embeddings"):
  with members:
    - "cover detection using dominant melody embeddings"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "towards a universal neural network encoder for time series"
    - "in defense of the triplet loss for person re identification"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "ensemble based cover song detection"):
  with members:
    - "large scale cover song detection in digital music libraries using metadata lyrics and audio features"
    - "robust downbeat tracking using an ensemble of convolutional networks"
    - "cover song identification with timbral shape sequences"

Connected through hyperedge HE-3 (induced by source paper "enhanced hierarchical music structure annotations via feature level similarity fusion"):
  with members:
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "multi scale geometric summaries for similarity based sensor fusion"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "geometric cross modal comparison of heterogeneous sensor data"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "invariant scattering convolution networks"
    - "classification with scattering operators"

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.IR(Information Retrieval)