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: "geodesic distance function learning via heat flow on vector fields"
  Abstract: Learning a distance function or metric on a given data manifold is of great importance in machine learning and pattern recognition. Many of the previous works first embed the manifold to Euclidean space and then learn the distance function. However, such a scheme might not faithfully preserve the distance function if the original manifold is not Euclidean. Note that the distance function on a manifold can always be well-defined. In this paper, we propose to learn the distance function directly on the manifold without embedding. We first provide a theoretical characterization of the distance function by its gradient field. Based on our theoretical analysis, we propose to first learn the gradient field of the distance function and then learn the distance function itself. Specifically, we set the gradient field of a local distance function as an initial vector field. Then we transport it to the whole manifold via heat flow on vector fields. Finally, the geodesic distance function can be obtained by requiring its gradient field to be close to the normalized vector field. Experimental results on both synthetic and real data demonstrate the effectiveness of our proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "neural embedding propagation on heterogeneous networks"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "user guided clustering in heterogeneous information networks via motif based comprehensive transcription"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "higher order organization of complex networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"

Connected through hyperedge HE-2 (induced by source paper "the vector heat method"):
  with members:
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "geodesics in heat a new approach to computing distance based on heat flow"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-3 (induced by source paper "relationship profiling over social networks reverse smoothness from similarity to closeness"):
  with members:
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-4 (induced by source paper "an ensemble diversity approach to supervised binary hashing"):
  with members:
    - "learning hashing with affinity based loss functions using auxiliary coordinates"
    - "hashing with binary autoencoders"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "a general two step approach to learning based hashing"
    - "distributed optimization of deeply nested systems"
    - "self taught hashing for fast similarity search"
    - "solving multiclass learning problems via error correcting output codes"

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.LG(Machine Learning)