human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "an ensemble diversity approach to supervised binary hashing"):
  Abstract: Binary hashing is a well-known approach for fast approximate nearest-neighbor search in information retrieval. Much work has focused on affinity-based objective functions involving the hash functions or binary codes. These objective functions encode neighborhood information between data points and are often inspired by manifold learning algorithms. They ensure that the hash functions differ from each other through constraints or penalty terms that encourage codes to be orthogonal or dissimilar across bits, but this couples the binary variables and complicates the already difficult optimization. We propose a much simpler approach: we train each hash function (or bit) independently from each other, but introduce diversity among them using techniques from classifier ensembles. Surprisingly, we find that not only is this faster and trivially parallelizable, but it also improves over the more complex, coupled objective function, and achieves state-of-the-art precision and recall in experiments with image retrieval.

Includes members:
  Member "learning hashing with affinity based loss functions using auxiliary coordinates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parmac distributed optimisation of nested functions with application to binary autoencoders")
  Member "hashing with binary autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "auto encoding twin bottleneck hashing")
  Member "fast supervised hashing with decision trees for high dimensional data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probability weighted compact feature for domain adaptive retrieval")
  Member "geodesic distance function learning via heat flow on vector fields":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural embedding propagation on heterogeneous networks")
  Member "a general two step approach to learning based hashing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep ordinal hashing with spatial attention")
  Member "distributed optimization of deeply nested systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "layer wise conditioning analysis in exploring the learning dynamics of dnns")
  Member "self taught hashing for fast similarity search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning compressed sentence representations for on device text processing")
  Member "solving multiclass learning problems via error correcting output codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "crowd scene analysis by output encoding")

Question: Please tell me which class the source paper 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.LG(Machine Learning)