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: "the icstm tum up approach to the 3rd chime challenge single channel lstm speech enhancement with multi channel correlation shaping dereverberation and lstm language models"
  Abstract: This paper presents our contribution to the 3rd CHiME Speech Separation and Recognition Challenge. Our system uses Bidirectional Long Short-Term Memory (BLSTM) Recurrent Neural Networks (RNNs) for Single-channel Speech Enhancement (SSE). Networks are trained to predict clean speech as well as noise features from noisy speech features. In addition, the system applies two methods of dereverberation on the 6-channel recordings of the challenge. The first is the Phase-Error based Filtering (PEF) that uses time-varying phase-error filters based on estimated time-difference of arrival of the speech source and the phases of the microphone signals. The second is the Correlation Shaping (CS) that applies a reduction of the long-term correlation energy in reverberant speech. The Linear Prediction (LP) residual is processed to suppress the long-term correlation. Furthermore, the system employs a LSTM Language Model (LM) to perform N-best rescoring of recognition hypotheses. Using the proposed methods, an improved Word Error Rate (WER) of 24.38% is achieved over the real eval test set. This is around 25% relative improvement over the challenge baseline.

Connected through hyperedge HE-1 (induced by source paper "a deep recurrent framework for cleaning motion capture data"):
  with members:
    - "recurrent network models for human dynamics"

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.SD(Sound)