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: "study of humanoid push recovery based on experiments"
  Abstract: Human can negotiate and recovers from Push up to certain extent. The push recovery capability grows with age (a child has poor push recovery than an adult) and it is based on learning. A wrestler, for example, has better push recovery than an ordinary man. However, the mechanism of reactive push recovery is not known to us. We tried to understand the human learning mechanism by conducting several experiments. The subjects for the experiments were selected both as right handed and left handed. Pushes were induced from the behind with close eyes to observe the motor action as well as with open eyes to observe learning based reactive behaviors. Important observations show that the left handed and right handed persons negotiate pushes differently (in opposite manner). The present research describes some details about the experiments and the analyses of the results mainly obtained from the joint angle variations (both for ankle and hip joints) as the manifestation of perturbation. After smoothening the captured data through higher order polynomials, we feed them to our model which was developed exploiting the physics of an inverted pendulum and configured it as a representative of the subjects in the Webot simulation framework available in our laboratory. In each cases the model also could recover from the push for the same rage of perturbation which proves the correctness of the model. Hence the model now can provide greater insight to push recovery mechanism and can be used for determining push recovery strategy for humanoid robots. The paper claimed the push recovery is software engineering problem rather than hardware.

Connected through hyperedge HE-1 (induced by source paper "an effective approach for mobile ad hoc network via i watchdog protocol"):
  with members:
    - "machine learning approach for text and document mining"
    - "an effective approach for mobile ad hoc network via i watchdog protocol"
    - "real time face recognition using adaboost improved fast pca algorithm"
    - "accurate location estimation of moving object with energy constraint a adaptive update algorithms to save data"

Connected through hyperedge HE-2 (induced by source paper "an effective approach for mobile ad hoc network via i watchdog protocol"):
  with members:
    - "an effective approach for mobile ad hoc network via i watchdog protocol"
    - "machine learning approach for text and document mining"
    - "real time face recognition using adaboost improved fast pca algorithm"
    - "accurate location estimation of moving object with energy constraint a adaptive update algorithms to save data"

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.RO(Robotics)