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: "discovering sound concepts and acoustic relations in text"
  Abstract: In this paper we describe approaches for discovering acoustic concepts and relations in text. The first major goal is to be able to identify text phrases which contain a notion of audibility and can be termed as a sound or an acoustic concept. We also propose a method to define an acoustic scene through a set of sound concepts. We use pattern matching and parts of speech tags to generate sound concepts from large scale text corpora. We use dependency parsing and LSTM recurrent neural network to predict a set of sound concepts for a given acoustic scene. These methods are not only helpful in creating an acoustic knowledge base but also directly help in acoustic event and scene detection research in a variety of ways.

Connected through hyperedge HE-1 (induced by source paper "learning sound events from webly labeled data"):
  with members:
    - "co sampling training robust networks for extremely noisy supervision"
    - "joint optimization framework for learning with noisy labels"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "decoupling when to update from how to update"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
    - "understanding deep learning requires rethinking generalization"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-2 (induced by source paper "unnamed entity recognition of sense mentions"):
  with members:
    - "an attentive neural architecture for fine grained entity type classification"
    - "neural architectures for named entity recognition"
    - "named entity recognition with bidirectional lstm cnns"
    - "boosting named entity recognition with neural character embeddings"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-3 (induced by source paper "sense perception common sense relationships"):
  with members:
    - "an attentive neural architecture for fine grained entity type classification"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "named entity recognition with bidirectional lstm cnns"
    - "boosting named entity recognition with neural character embeddings"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-4 (induced by source paper "nels never ending learner of sounds"):
  with members:
    - "an approach for self training audio event detectors using web data"
    - "audio event detection using weakly labeled data"
    - "audiopairbank towards a large scale tag pair based audio content analysis"

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)